Cross-Domain Decentralized Graph Learning (CD-DGL) is a promising paradigm that enables efficient, privacy-preserving collaboration among multiple parties to unlock the value of cross-domain graph data. However, it faces two fundamental challenges. First, inconsistent label spaces across domains drive local models to learn domain-specific biases, which means domain-invariant topological knowledge extraction beyond label constraints is difficult. Second, existing domain topology shift and heterogeneous model architectures make direct model aggregation infeasible. To address these issues, we first use Extended Persistent Homology (EPH) to reveal and quantify the problem of domain topology shift induced by the cross-domain setting. Building on this insight, we present Decentralized Graph Learning with Topology-Aware Knowledge Fusion (DGTF), a novel framework designed to facilitate positive topological knowledge transfer in CD-DGL. Our framework achieves this by integrating two core strategies: first, a contrastive learning-based approach to extract task-agnostic topological knowledge, and second, a topology-aware, model-independent knowledge fusion method to effectively integrate this topological information. Extensive experiments conducted under various cross-domain and model-heterogeneous settings validate the superiority and effectiveness of our proposed framework.

Federated fine-tuning of Mixture-of-Experts (MoE)-based large language models (LLMs) is challenging due to their massive computational requirements and the resource constraints of participants. Existing works attempt to fill this gap through model quantization, computation offloading, or expert pruning. However, they cannot achieve desired performance due to impractical system assumptions and a lack of consideration for MoE-specific characteristics. In this paper, we propose Flux, a system designed to enable federated fine-tuning of MoE-based LLMs across participants with constrained computing resources (e.g., consumer-grade GPUs), aiming to minimize time-to-accuracy. Flux introduces three key innovations: (1) quantization-based local profiling to estimate expert activation with minimal overhead, (2) adaptive layer-aware expert merging to reduce resource consumption while preserving accuracy, and (3) dynamic expert role assignment using an exploration-exploitation strategy to balance tuning and non-tuning experts. Extensive experiments on LLaMA-MoE and DeepSeek-MoE with multiple benchmark datasets demonstrate that Flux significantly outperforms existing methods, achieving up to 4.75× speedup in time-to-accuracy.

The openness of wireless networks enables flexible connections on devices, but also arises the security and privacy concerns on adversarial eavesdropping. By concealing the transmissions from eavesdropper, covert communication preserves the privacy of communications and has become a significant research topic. Whereas, existing works primarily focus on the covertness of communication links, neglecting the protection for the critical leader nodes response for information aggregation and decision making in information systems. To bridge this gap, we introduce a novel conceptcovert leader, whose identity remains concealed from all other nodes while performing leadership functions. This design inherently mitigates targeted attacks (e.g., eavesdropping, jamming, DoS) against leaders, enhancing both privacy and robustness of wireless network. Then, a distributed randomizedK-Covert Leader Election (KCLE) algorithm is designed, in whichKis a hyperparameter to depict the covertness of leader election and can be adjusted when KCLE algorithm is implemented in reality. Unlike cryptographic solutions, KCLE leverages physical-layer signal properties to elect a leader from at leastKcandidates with balanced energy-time tradeoffs, ensuring no leader identity leakage occurs during election. The algorithm operates without complex encryption, making it suitable for resource-constrained Internet-of-Things and ad hoc networks. Theoretical analysis and simulation results demonstrate KCLE’s correctness, covertness guarantees, and operational efficiency under SINR interference models.

Log-structured merge (LSM) tree-based key-value (KV) stores organize writes into hierarchical batches to optimize write performance. However, the notorious compaction process and multi-level query mechanism of LSM-tree severely hurt system performance. Our preliminary experiments show that (1) When compaction occurs in the L0 and L1 of the LSM-tree, it may saturate system computation and memory resources, ultimately causing the entire system to stall, and (2) large number of iterative retrievals across multiple levels is usually required to locate the queried data, while redundant key range overlap in L0 further increases the overhead. Based on these observations, we introduce Re-LSM+, a ReRAM-based Processing-in-Memory framework for LSM-based Key-Value Stores. In Re-LSM+, we offload compaction tasks from the higher levels of the LSM-tree to the PIM processing part. A highly parallel ReRAM compaction accelerator is designed by breaking down the three-phase compaction process into basic logic operations. Additionally, we design an index table and a multi-layer Bloom filter for different levels to improve the query efficiency of the LSM-tree. Evaluation results from db_bench show that Re-LSM+ achieves a 2.37× improvement in random write throughput compared to RocksDB. Furthermore, the ReRAM-based compaction accelerator achieves a 68.16× speedup over the CPU-based implementation and reduces energy consumption to 25.5×.

The decentralized nature of federated learning (FL) makes it difficult to verify the trustworthiness of participating clients, creating an opportunity for backdoor attacks. This paper addresses a general backdoor-resilient decentralized FL problem without any prior knowledge of the type of backdoor attacks or information about malicious clients. After an in-depth investigation of how backdoor attacks are conducted in FL, we introduce a multi-armed bandit-based knowledge distillation approach to help benign clients learn useful knowledge from other clients while rejecting potential backdoors hidden in shared updates. Unlike most previous works that rely on identifying and removing malicious updates—an approach limited to scenarios with fewer than 50% attackers—our knowledge distillation technique enables benign clients to reject backdoored knowledge while preserving useful information, maintaining effective defense even when malicious clients exceed 50% of the population. Additionally, to handle the various updates from clients with Non-IID dataset, a multi-armed bandit scheme is designed for each benign client to select the most appropriate teachers for knowledge distillation, resulting in high accuracy and fast convergence. Extensive experiments demonstrate that our multi-armed bandit-based knowledge distillation approach achieves high accuracy and general backdoor resilience. Comparisons with previous works show that our approach can reduce the attack success rate by 14.71%∼96.78% on average.

In this work, we study to release the potential of massive heterogeneous weak computing power to collaboratively train large-scale models on dispersed datasets. In order to improve both efficiency and accuracy in resource-adaptive collaborative learning, we take the first step to consider the unstructured pruning, varying submodel architectures, knowledge loss, and straggler challenges simultaneously. We propose a novel semiasynchronous collaborative training framework, namely Co-S2P, with data distribution-aware structured pruning and cross-block knowledge transfer mechanism to address the above concerns. Furthermore, we provide theoretical proof that Co-S2P can achieve asymptotic optimal convergence rate of O(1/√ N∗EQ). Finally, we conduct extensive experiments on two types of tasks with a real-world hardware testbed including diverse IoT devices. The experimental results demonstrate that Co-S2P improves accuracy by up to 8.8% and resource utilization by up to 1.2× compared to state-of-the-art methods, while reducing memory consumption by approximately 22% and training time by about 24% on all resource-limited devices.

As a combination of digital twin and edge computing, the digital twin edge networks (DITENs) have gained significant interest due to their bridging roles between physical edge networks and digital systems. The Device-to-Device (D2D) communication acts as an ongoing transmission paradigm for DITENs since it facilitates interoperability between nearby wireless entities and enhances the spectrum utilization and overall throughput via direct link. However, most existing works on the D2D communication are related to the centralized ones, and consider the mobility and interference management separately, which cannot be used for the dynamic scenarios in DITENs. In this paper, we consider the D2D communication problem in dynamic DITENs in the context of the SINR model, and propose a dynamicity-tolerant D2D communication algorithm in a distributed manner. Using an oblivious transmission strategy, our algorithm can complete the D2D communication among$n$entities within$O(\log n)$time steps despite the various dynamic factors, which achieves asymptotically optimal time complexity. It is asserted that the D2D communication algorithm is asymptotically optimal, given that the lower bound for successful message dissemination is$\Omega (\log n)$. Rigorous theoretical analyses and empirical results are conducted to show the correctness and efficiency of our proposed algorithm.

In recent years, volumetric video streaming has emerged as a key application in the field of virtual reality (VR) and augmented reality (AR), attracting growing research and industry interest. Despite its potential, the extremely high bandwidth requirements of volumetric content far exceed the capacity of current networks to support full-resolution streaming. To address this limitation, industry practitioners often employ field-of-view (FoV) prediction to downscale the streaming content based on user gaze direction. Although this approach makes the streaming feasible, our empirical measurement reveals that, working with the FoV prediction, the commonly used sequential prefetching mechanism severely constrains streaming efficiency. To overcome this bottleneck, we introduce PACE, a smart prefetching strategy built on multi-round cell-level download scheduling. PACE divides the prefetching process of each group-of-frames (GoF) into several rounds, where distinct video cells are downloaded in each round according to periodically updated FoV predictions. This design decouples FoV prediction from the constraint of prefetch length, enabling more adaptive and responsive streaming. Moreover, PACE integrates a greedy quality selection policy that dynamically adjusts video quality to make full use of available bandwidth and enhance the overall quality of experience (QoE). Comprehensive evaluations show that PACE improves FoV prediction accuracy by up to 23.1% and boosts QoE by as much as 54.8%, while maintaining robust performance across diverse network and user scenarios.

This paper studies an efficient federated learning (FL) problem involving multiple edge-based clients with heterogeneous constrained resources. Compared with numerous training parameters, the computing and communication resources of clients in edge scenarios are usually insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem, which delays the convergence of FL. To address these issues, we proposeFed-RAA: aResource-AdaptiveAsynchronousFederated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates submodels of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned submodel and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, an online greedy-based algorithm is designed for asynchronous submodel assignment in Fed-RAA, improving the convergence of Fed-RAA by optimal minimization on the training delay bound of submodels. Compared to state-of-the-art methods, our Fed-RAA algorithm reduces the time required to achieve the target accuracy by an average of$ 30.89\%$, demonstrating its superior efficiency on heterogeneous constrained computing and communication resources. To the best of our knowledge, this paper is the first resource-adaptive asynchronous method for submodel-based FL with guaranteed theoretical convergence.

Ranking a given set of items is a fundamental problem with widespread applications in information retrieval, recommendation systems, and beyond. Crowdsensing data trading (CDT) systems provide an effective means to collect opinions from distributed workers for ranking tasks. However, these systems face several challenges, including preserving privacy, maintaining ranking accuracy, optimizing worker participation, and ensuring incentive compatibility for all stakeholders. To address these challenges, we propose OPAR-IC, an integrated framework designed to enhance efficiency and privacy in CDT systems. The proposed framework integrates a multi-armed bandit (MAB) based approach to dynamically adjust ranking granularity, a hybrid privacy-preserving mechanism combining logarithmic and binary methods to safeguard worker data, and a combinatorial MAB model for efficient worker recruitment. To enhance worker participation and ensure incentive compatibility among all stakeholders, we integrate a hierarchical Stackelberg game into the framework, balancing competing incentives and achieving equilibrium. Our approach is thoroughly validated through theoretical analysis, offering theoretical guarantees for privacy, ranking accuracy, worker participation, and incentive compatibility. Extensive experiments on real-world datasets demonstrate significant improvements in adaptive ranking accuracy, privacy preservation, worker recruitment, and three-party incentive compatibility.

Byzantine resilience is essential in federated learning (FL) to safeguard model training from malicious or faulty participants. However, existing Byzantine-resilient methods struggle when faced with heavy-tailed gradient noise, a common challenge in heterogeneous environments. In this work, we propose a Byzantine-resilient FL framework specifically designed to handle both heterogeneity and heavy-tailed noise. Our approach builds on robust distributed stochastic heavy-ball optimization, incorporating update normalization and gradient/momentum clipping to mitigate the effects of heavy-tailed noise. We establish the first high-probability convergence guarantees for Byzantine-resilient FL under these conditions, showing that our algorithms achieve optimal Byzantine resilience and align with known lower bounds. Additionally, we introduce an efficient variant of the nearest neighbor mixing technique, leveraging random projections to significantly reduce computational costs in high-dimensional settings. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that our methods outperform existing approaches in robustness against both Byzantine failures and heavy-tailed noise.

The linkschedulingis to determine which links should be scheduled at what times, each of which is a (sender, receiver) communication pair used to realize wireless transmission of a message. It is an appealing solution to fulfill the resource-constrained and latency requirements of unmanned aerial vehicle (UAV) swarms. However, most existing link scheduling algorithms primarily focus on the throughput or latency in wireless communication, neglecting the protection against the eavesdropping attack due to the openness of UAV swarms, thereby leading to unsafe message dissemination. To address this issue, we consider integrating the covert communication into the link scheduling by concealing the very existence of message transmission. We first present a successive interference cancellation-based link diversity partition (SIC-LDP) algorithm in the context of the physical interference model, to maximize the number of concurrently transmitting links in a single time round, also called the maximum link scheduling (MLS) issue. Then, using a friendly jammer, we further propose a covertness-aware SIC-LDP algorithm (named SIC-CLDP) to resolve the MLS, as well as to defend against the eavesdropping attack. Both SIC-LDP and SIC-CLDP algorithms are proven to have constant approximation ratios. Next, implementing SIC-LDP and SIC-CLDP repeatedly can minimize the number of time rounds schedulingnparticipating links, termed the shortest link scheduling (SLS) issue, with an approximation ratio ofO(lnn). Lastly, theoretical proofs and simulation results demonstrate the correctness, covertness, and efficiency of our proposed algorithms under the eavesdropping attack.

The growing resource demands of large-scale transformer models pose significant challenges for resource-constrained users, particularly in distributed environments. To address this issue, we propose a federated learning framework called Fed-Grow, which enables multiple participants to collaboratively learn a lightweight scaling operation that transfers knowledge from pretrained small models to a large transformer model. In Fed-Grow, we introduce the Dual-LiGO (Dual Linear Growth Operator) architecture, consisting of Local-LiGO and Global-LiGO components. Local-LiGO addresses model heterogeneity by adapting each participant's pre-trained model to a common intermediate form, while Global-LiGO facilitates knowledge sharing across participants without sharing local models or raw data, ensuring privacy preservation. This federated approach offers a scalable solution for growing large transformers in a distributed manner, where only the Global-LiGO is shared, significantly reducing communication overhead while maintaining comparable model performance under the same communication constraints. Experimental results demonstrate that Fed-Grow outperforms state-of-the-art methods in terms of accuracy and precision, while reducing the number of trainable parameters by 59.25% and communication costs by 73.01%. These improvements allow for higher efficiency in training large models in distributed environments, without sacrificing performance. To the best of our knowledge, Fed-Grow is the first method that enables cooperative transformer scaling in a distributed setting, making it a practical solution for resource-constrained users.

Abstract Hypergraph decomposition is a fundamental problem in hypergraph analysis which breaks down hypergraphs into cohesive subgraphs and functional units with dense interactions. Hyperedge intersections and overlaps capture the unique property of shared elements (vertices) between groups (hyperedges) in hypergraphs, revealing cohesive substructures not apparent when focusing solely on individual connections. Despite the significance of hyperedge overlap as a measure of hypergraph cohesiveness, existing models for hypergraph decomposition fail to capture this feature. In this paper, we study the problem of hypergraph decomposition with intersection bounds. We propose the ( k , s )-core, a new cohesive subgraph model incorporating both a vertex degree constraint k and a hyperedge intersection constraint s . This model includes two types: (1) strong ( k , s )-cores, where connected hyperedges share at least s vertices, enforcing strong hyperedge overlap, and (2) weak ( k , s )-cores, where hyperedges are connected through s -walks, allowing for a looser overlap. We prove that our definition of ( k , s )-cores exhibits uniqueness and hierarchical properties. Based on the properties, we develop two decomposition algorithms: a bottom-up algorithm for strong ( k , s )-cores, which uses a heuristic hyperedge removal mechanism to maintain consistent decomposition results and employs a union-find data structure for efficient connectivity identification, and a top-down algorithm for weak ( k , s )-cores that preserves the subgraph containment relationship. Our algorithms achieve traversal efficiency by processing each hyperedge in the hypergraph only once. Additionally, all ( k , s )-cores can be efficiently stored with minimal memory overhead. Comprehensive experiments and case studies show that the ( k , s )-core model outperforms existing methods in capturing cohesive subgraphs with overlaps in hypergraphs. Furthermore, the proposed algorithms demonstrate high efficiency and scalability, making them well-suited for real-world hypergraphs.

Network traffic analysis attacks represent a significant threat to information security and user privacy. Leveraging machine learning, these attacks can infer sensitive information even when encryption and anonymization techniques are in place. This paper proposes TravelingTogether, a novel framework that leverages packet aggregation from multiple source hosts to defend users against network traffic analysis. TravelingTogether works at the network level, providing transparent and seamless protection to any hosts connected to the network. We evaluate our defense across different scenarios through a comprehensive set of experiments, demonstrating its effectiveness in thwarting website fingerprinting attacks as a representative use case, and its efficiency in terms of low time and bandwidth overhead.

Community search, which finds cohesive subgraphs containing given query vertices, has attracted much attention in decades. On attributed graphs, when considering the fairness of members' attributes in a community, the cohesiveness constraint of a clique is too strong, which often causes no fair clique based communities can be found. Thus, in this paper, we use the k-truss model, which is a relaxation of the clique but whose members have large engagement and high tie strength, to describe fair communities, namely fair k-truss communities (FTC) and anchored fair k-truss communities (AFTC, using anchored vertices to help satisfying the fairness constraint). We formulate the FTC and AFTC search problems to find the FTC or AFTC containing a given query vertex$q$which has the largest$k$and the smallest diameter. We prove the hardness of both problems. We develop several greedy algorithms and acceleration strategies to solve FTC and AFTC search problems. Experiments on 8 real-world networks show the significance of our FTC and AFTC models, and high performance of our algorithms and acceleration strategies.

The InterPlanetary File System (IPFS) is a prominent decentralized storage solution; however, it struggles with performance issues. To address this challenge, the IPFS team has patched a series of centralized components, resulting in improved performance while giving more significant roles to specific entities. Balancing speed and decentralization has always posed a complex dilemma for storage systems. In this paper, we conduct a series of experiments and analyses to identify the performance advantages and constraints associated with the IPFS decentralized protocol. Based on thorough analysis, we propose a novel scheme named XIPFS to optimize IPFS. This scheme includes facilitating parallel block exchange across multiple nodes, refining content Publication strategies, and improving node selection algorithms for content routing. Our goal is to maximize the benefits of decentralized multi-source parallel downloading while minimizing the negative impact of decentralized indexing on execution time. Compared to previous approaches, the proposed optimizations are lightweight and fully compatible with the decentralized protocol, enabling autonomous execution and utility realization at each node. Experimental results demonstrate that XIPFS significantly improves node performance without compromising decentralization.

End-to-end navigation models commonly incorporate multiple sub-modules, each designed for distinct purposes such as searching, obstacle avoidance, and target localization. However, agents equipped with these modules may still struggle to apply the appropriate strategies at the right locations and stages. For instance, agent might incorrectly rely on the search or localization module for obstacle avoidance, reducing adaptability in dynamic environments. Additionally, existing methods assume the recognition for target object is always correct, neglecting the unavoidable misclassification caused by visually similar objects. To apply appropriate strategy for a given situation, we introduce Adaptive Strategy Feature Fusion (ASFF). It heuristically assigns appropriate weights to different sub-modules based on current observation and memory state, enabling flexible integration with arbitrary sub-module combinations. To improve localization in the presence of misclassification, we propose Fault Tolerant Target Memory Aggregator (FTTMA), a module that uses clustering-based sparse self-attention and target cross-attention to minimize interference from misclassified object, providing accurate target orientation to the agent. Experiments on the AI2THOR and RoboTHOR datasets, including both typical and zero-shot navigation tasks, demonstrate that our model outperforms the state-of-the-art (SOTA) methods in both success rate and navigation efficiency.

This paper examines the theoretical performance of distributed diffusion models in environments where computational resources and data availability vary significantly among workers. Traditional models centered on single-worker scenarios fall short in such distributed settings, particularly when some workers are resource-constrained. This discrepancy in resources and data diversity challenges the assumption of accurate score function estimation foundational to single-worker models. We establish the inaugural generation error bound for distributed diffusion models in resource-limited settings, establishing a linear relationship with the data dimension $d$ and consistency with established single-worker results. Our analysis highlights the critical role of hyperparameter selection in influencing the training dynamics, which are key to the performance of model generation. This study provides a streamlined theoretical approach to optimizing distributed diffusion models, paving the way for future research in this area.

This paper investigates decentralized unlearning, aiming to eliminate the impact of a specific client on the whole decentralized system. However, decentralized communication characterizations pose new challenges for effective unlearning: the indirect connections make it difficult to trace the specific client's impact, while the dynamic topology limits the scalability of retraining-based unlearning methods. In this paper, we propose the first **P**rovable **D**ecentralized **U**nlearning algorithm under **D**ynamic **T**opologies called PDUDT. It allows clients to eliminate the influence of a specific client without additional communication or retraining. We provide rigorous theoretical guarantees for PDUDT, showing it is statistically indistinguishable from perturbed retraining. Additionally, it achieves an efficient convergence rate of $\mathcal{O}(\frac{1}{T})$ in subsequent learning, where $T$ is the total communication rounds. This rate matches state-of-the-art results. Experimental results show that compared with the Retrain method, PDUDT saves more than 99\% of unlearning time while achieving comparable unlearning performance.

Arrhythmia diagnosis using electrocardiogram (ECG) is critical for preventing cardiovascular risks. However, existing deep learning-based methods struggle with label scarcity and contrastive learning-based methods suffer from false-negative samples, which lead to poor model generalization. Besides, due to inter-subject variability, pre-trained models cannot achieve evenly performance across individuals. Conducting model fine-tuning for each individual is computationally expensive and does not guarantee improvement. We propose DiffECG, a diffusion-based self-supervised learning framework for label-efficient and personalized arrhythmia detection. Our method utilizes a diffusion model to extract robust ECG representations, coupled with a novel feature extractor and a multi-modal feature fusion strategy to obtain a well-generalized model. Moreover, we propose an efficient model personalization mechanism based on zeroth-order optimization. It personalizes the model by tuning the noise-adding step t in the diffusion process, significantly reducing computational costs compared to model fine-tuning. Experimental results show that our proposed method outperforms the SOTA method by 37.9% and 23.9% in generalization and personalization performance, respectively. The source code is available at: https://github.com/Auguuust/DiffEC

We investigate the problem of finding second-order stationary points (SOSP) in differentially private (DP) stochastic non-convex optimization. Existing methods suffer from two key limitations: \textbf{(i)} inaccurate convergence error rate due to overlooking gradient variance in the saddle point escape analysis, and \textbf{(ii)} dependence on auxiliary private model selection procedures for identifying DP-SOSP, which can significantly impair utility, particularly in distributed settings. To address these issues, we propose a generic perturbed stochastic gradient descent (PSGD) framework built upon Gaussian noise injection and general gradient oracles. A core innovation of our framework is using model drift distance to determine whether PSGD escapes saddle points, ensuring convergence to approximate local minima without relying on second-order information or additional DP-SOSP identification. By leveraging the adaptive DP-SPIDER estimator as a specific gradient oracle, we develop a new DP algorithm that rectifies the convergence error rates reported in prior work. We further extend this algorithm to distributed learning with heterogeneous data, providing the first formal guarantees for finding DP-SOSP in such settings. Our analysis also highlights the detrimental impacts of private selection procedures in distributed learning under high-dimensional models, underscoring the practical benefits of our design. Numerical experiments on real-world datasets validate the efficacy of our approach.

Inspired by human-like behaviors for navigation: first searching to explore unknown areas before discovering the target, and then the pathfinding of moving towards the discovered target, recent studies design parallel submodules to achieve different functions in the searching and pathfinding stages, while ignoring the differences in reward signals between the two stages. As a result, these models often cannot be fully trained or are overfitting on training scenes. Another bottleneck that restricts agents from learning two-stage strategies is spatial perception ability, since the studies used generic visual encoders without considering the depth information of navigation scenes. To release the potential of the model on strategy learning, we propose the Two-Stage Reward Mechanism (TSRM) for object navigation that decouples the searching and pathfinding behaviours in an episode, enabling the agent to explore larger area in searching stage and seek the optimal path in pathfinding stage. Also, we propose a pretraining method Depth Enhanced Masked Autoencoders (DE-MAE) that enables agent to determine explored and unexplored areas during the searching stage, locate target object and plan paths during the pathfinding stage more accurately. In addition, we propose a new metric of Searching Success weighted by Searching Path Length (SSSPL) that assesses agent’s searching ability and exploring efficiency. Finally, we evaluated our method on AI2-Thor and RoboTHOR extensively and demonstrated it can outperform the state-of-the-art (SOTA) methods in both the success rate and the navigation efficiency.

Federated Learning(FL) is currently a widely used collaborative learning framework, and the distinguished feature of FL is that the clients involved in training do not need to share raw data, but only transfer the model parameters to share knowledge, and finally get a global model with improved performance. However, recent studies have found that sharing model parameters may still lead to privacy leakage. From the shared model parameters, local training data can be reconstructed and thus lead to a threat to individual privacy and security. We observed that most of the current attacks are aimed at client-specific data reconstruction, while limited attention is paid to the information leakage of the global model. In our work, we propose a novel FL attack based on shared model parameters that can deduce the data distribution of the global model. Different from other FL attacks that aim to infer individual clients’ raw data, the data distribution inference attack proposed in this work shows that the attackers can have the capability to deduce the data distribution information behind the global model. We argue that such information is valuable since the training data behind a well-trained global model indicates the common knowledge of a specific task, such as social networks and e-commerce applications. To implement such an attack, our key idea is to adopt a deep reinforcement learning approach to guide the attack process, where the RL agent adjusts the pseudo-data distribution automatically until it is similar to the ground truth data distribution. By a carefully designed MDP process, our implementation ensures our attack can have stable performance and experimental results verify the effectiveness of our proposed inference attack.

Meta computing, as an innovative computing paradigm, aims to transform the Internet into a vast and distributed computing resource pool. This paradigm holds significant promise for the Industrial Internet of Things (IIoT), offering efficient, fault-tolerant, and personalized services while ensuring strong security and privacy. Nowadays, content delivery networks (CDNs) are integral to this vision, providing critical network support by reducing latency, alleviating network congestion, and enhancing service quality. Accurate prediction of CDN cache group performance, which involves heterogeneous edge servers handling diverse workloads, is essential for optimal resource utilization, dynamic load balancing, and efficient traffic management in IIoT. This article addresses the challenge of performance prediction in CDNs using machine learning techniques. By leveraging business request data, load information, and other relevant features, our approach aims to predict key performance indicators, such as CPU utilization, bandwidth usage, and I/O operations. We propose a comprehensive feature engineering method that aggregates input metrics across devices, categorizes business requests using clustering, and incorporates time series modeling to capture traffic patterns. Extensive experiments demonstrate the effectiveness of our approach, highlighting its potential to enhance resource management and service quality in CDNs, thereby supporting the deployment of meta computing in IIoT.

Byzantine fault-tolerant (BFT) consensus is a critical problem in parallel and distributed computing systems, particularly with potential adversaries. Most prior work on BFT consensus assumes reliable message delivery and tolerates arbitrary failures of up to$\frac{n}{3}$nodes out of$n$total nodes. However, many systems face unpredictable message delivery failures. This paper investigates the impact of unpredictable message delivery failures on the BFT consensus problem. We propose Nicaea, a novel protocol enabling consensus among loyal nodes when the number of Byzantine nodes is below a new threshold, given by:$\frac{\left(2-\rho\right)\left(1-\rho\right)^{2n-2}-1}{\left(2-\rho\right) \left(1-\rho\right)^{2n-2}+1}n$, where$\rho$denotes the message failure rate. Theoretical proofs and experimental results validate Nicaea's Byzantine resilience. Our findings reveal a fundamental trade-off: as message delivery instability increases, a system's tolerance to Byzantine failures decreases. The well-known$\frac{n}{3}$threshold under reliable message delivery is a special case of our generalized threshold when$\rho=0$. To the best of our knowledge, this work presents the first quantitative characterization of unpredictable message delivery failures’ impact on Byzantine fault tolerance in parallel and distributed computing.

Federated Learning (FL) is a new learning framework in which$s$clients collaboratively train a model under the guidance of a central server. Meanwhile, with the advent of the era of large models, the parameters of models are facing explosive growth. Therefore, it is important to design federated learning algorithms for edge environment. However, the edge environment is severely limited in computing, storage, and network bandwidth resources. Concurrently, adaptive gradient methods show better performance than constant learning rate in non-distributed settings. In this paper, we propose a pruning-based distributed Adam (PD-Adam) algorithm, which combines model pruning and adaptive learning steps to achieve asymptotically optimal convergence rate of$O(1/\sqrt[4]{K})$. At the same time, the algorithm can achieve convergence consistent with the centralized model. Finally, extensive experiments have confirmed the convergence of our algorithm, demonstrating its reliability and effectiveness across various scenarios. Specially, our proposed algorithm is$2$% and$18$% more accurate than the current state-of-the-art FedAvg algorithm on the ResNet and CIFAR datasets.

Decentralized federated learning (DFL) has gained significant attention due to its ability to facilitate collaborative model training without relying on a central server. However, it is highly vulnerable to backdoor attacks, where malicious participants can manipulate model updates to embed hidden functionalities. In this paper, we propose BaDFL, a novel Backdoor Attack defense mechanism for Decentralized Federated Learning. BaDFL enhances robustness by applying strategic model clipping at the local update level. To the best of our knowledge, BaDFL is the first decentralized federated learning algorithm with theoretical guarantees against model poisoning attacks. Specifically, BaDFL achieves an asymptotically optimal convergence rate of$O(\frac{1}{\sqrt{nT}})$, wherenis the number of nodes andTis the global maximum iteration number. Furthermore, we provide a comprehensive analysis under two different attack scenarios, showing that BaDFL maintains robustness within a specific defense radius. Extensive experimental results show that, on average, BaDFL can effectively defend against model poisoning within 6 mitigation rounds, with less than a 1% drop in accuracy.

Nonvolatile field-programmable gate arrays (FPGAs) offer advantages in terms of high logic density and near-zero leakage power when contrasted with conventional static random access memory-based FPGAs. However, they have a lifetime issue. To deal with this problem, a series of configuration files can be generated with various logical-to-physical mappings. This enables intensive writing to be distributed across different physical regions for wear leveling. Currently, the configuration files are independently generated, which is time consuming. In this article, we propose to investigate correlations and use them to assist the computer-aided design (CAD) flow to speed up the procedure of generating configuration files. First, we develop dynamic probabilities to drive the swapping of placement stage in CAD flow, so as to push components to locate appropriate positions quickly. Second, we design the congestion information inheritance strategy to adjust routing parameters in the routing stage, aiming to reduce the number of routing attempts. Evaluation shows that the proposed schemes can deliver 44.15% decrease in placement and routing runtime, while maintaining comparable performance and lifetime, when compared with existing strategies.

The openness of wireless networks opens the door to Byzantine attacks on the physical channels, making the communications unreliable and resulting in more challenges in achieving consensus among mobile devices. To address this issue, this paper studies the Byzantine-fault-tolerant (BFT) consensus problem based on an unreliable Byzantine communication model. Different from the previous works requiring stable communications between the honest nodes, considering the unreliable communication makes our problem more realistic but also harder. Based on the unreliable communication model, we first implement a BFT abstract MAC (absMAC) layer with a distributed and randomized multi-channel communication algorithm. In the implemented absMAC layer, its acknowledgement and progress operations can be completed within$O\left ({{\frac {kn}{k-f}\log n}}\right)$and$O\left ({{\frac {k}{k-f}\log n}}\right)$rounds, respectively. n, f, and k are the numbers of nodes, Byzantine nodes, and channels, respectively. With the implemented absMAC layer, an efficient and elegant BFT consensus algorithm is designed, which can solve the binary consensus problem within$O\left ({{\frac {kn}{k-f}\log n}}\right) ^{^{^{^{}}}}$rounds in expectation. Even though a series of works have discussed how to achieve consensus with a specific absMAC layer provided, to the best of our knowledge, this paper is the first one that implements a BFT absMAC layer.

Text-video retrieval aims to establish a matching relationship between a video and its corresponding text. However, previous works have primarily focused on salient video subjects, such as humans or animals, often overlooking Low-Salient but Discriminative Objects (LSDOs) that play a critical role in understanding content. To address this limitation, we propose a novel model that enhances retrieval performance by emphasizing these overlooked elements across video and text modalities. In the video modality, our model first incorporates a feature selection module to gather video-level LSDO features, and applies cross-modal attention to assign frame-specific weights based on relevance, yielding frame-level LSDO features. In the text modality, text-level LSDO features are captured by generating multiple object prototypes in a sparse aggregation manner. Extensive experiments on benchmark datasets, including MSR-VTT, MSVD, LSMDC, and DiDeMo, demonstrate that our model achieves state-of-the-art results across various evaluation metrics.

Vision-and-Language Navigation (VLN) is a challenging task where an agent is required to navigate to a natural language described location via vision observations. The navigation abilities of the agent can be enhanced by the relations between objects, which are usually learned using internal objects or external datasets. The relationships between internal objects are modeled employing graph convolutional network (GCN) in traditional studies. However, GCN tends to be shallow, limiting its modeling ability. To address this issue, we utilize a cross attention mechanism to learn the connections between objects over a trajectory, which takes temporal continuity into account, termed as Temporal Object Relations (TOR). The external datasets have a gap with the navigation environment, leading to inaccurate modeling of relations. To avoid this problem, we construct object connections based on observations from all viewpoints in the navigational environment, which ensures complete spatial coverage and eliminates the gap, called Spatial Object Relations (SOR). Additionally, we observe that agents may repeatedly visit the same location during navigation, significantly hindering their performance. For resolving this matter, we introduce the Turning Back Penalty (TBP) loss function, which penalizes the agent’s repetitive visiting behavior, substantially reducing the navigational distance. Experimental results on the REVERIE, SOON, Touchdown and R2R datasets demonstrate the effectiveness of the proposed method.

Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user’s quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system’s economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP’s performance closely approximates that of the optimal scheme and surpasses existing schemes.

Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intelligent control in terrestrial industrial cyber-physical systems, whose system-level privacy security can be strengthened via blockchain based consensus protocol. Moreover, digital twin (DT) can shorten the gap between physical unities and digital space to enable instant data mapping in SGTNs environments. However, complex multi-modal network environments, such as stochastic task size, dynamic low earth orbit location, and time-varying channel gains, hinder better performance metrics in terms of energy consumption, throughput and privacy overhead. Hence, we establish a SGTN integrated cloud-fog automation model to transfer task data to low earth orbit satellites, and then execute broad communication access, powerful computation offloading, and efficient twin control. Next, we propose a Lyapunov stability theory based multi-modal federated learning (LST-MMFL) method to optimize the battery energy, the size of block, computation frequency, and the number of twin control for minimizing the total energy consumption and privacy overhead. Furthermore, we design a novel blockchain based transaction verification protocol to strengthen privacy security, derive performance upper bounds of SGTN model, and fulfill the long-term average task as well as energy queue constraints. Finally, massive simulation results show that the proposed LST-MMFL algorithm outperforms existing state-of-the-art benchmarks in line with energy consumption, available battery level, networked control and privacy protection overhead.

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations.

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we proposeRomeoas the first work to exploit the mmWave radar forrotatingmachinery fault detection by extracting a fine-grained velocity signature. Though mmWave radars should capture instant rotation information with their claimed high sensitivity and sampling rate, direct adoption entails significant efforts for high-precision velocity measurement per radar to handle; particularly, exhausted system calibration and noise interference. To this end, we first develop a phase-velocity model to characterize the relationship between the mmWave signal phase and the fine-grained angular velocity. We then explore the geometric properties of specific positions in the rotation trajectory to precisely calibrate the rotation sensing model, leading to an iterative algorithm for accurate angular velocity measurement. Finally, we propose a simple yet effective fault detection algorithm by extracting a unique velocity signature. Our extensive experiments showRomeoachieves a median error of 0.4$^\circ$/s for fine-grained angular speed measurement, outperforming SOTA solutions with over ×16 angular speed granularity and ×7 measurement precision.

Fueled by Metaverse, 360° video streaming has seen tremendous growth in the past years. However, our measurement reveals that current 360° streaming systems suffer from a dilemma that severely limits QoE. On the one hand, viewport prediction requires the shortest possible prediction distance for high predicting accuracy; On the other hand, video transmission requires more buffered data to compensate for bandwidth fluctuations otherwise substantial playback rebuffering would be incurred. There is so far no existing method that can break this dilemma so the QoE optimization for 360° video streaming was naturally bottlenecked. This work is the first attempt to tackle this challenge by developing QUTA – a novel learning-based streaming system. Specifically, according to our measurement, three kinds of internal streaming parameters have significant impacts on the prediction distance, namely, download pause, data rate threshold, and playback rate. On top of this, we design a new long-term-planning (LTP) continuous control deep reinforcement learning method that tunes the parameters dynamically based on the network condition and the streaming context. Extensive evaluations based on real system prototypes show that QUTA not only improves the prediction accuracy and QoE performance by up to 68.4% but also exhibits strong temporal and spatial robustness.

Fueled by the rapid advances in high-speed mobile networks, live video streaming has seen explosive growth in recent years and some DASH-based algorithms were specifically proposed for low-latency video delivery. We conducted a measurement study for the state-of-the-art algorithms with large-scale network traces. It reveals that these algorithms are susceptible to network condition changes due to the use of solo universal adaptation logics, resulting in the playback latency that has substantial variations across highly fluctuating networks. To tackle this challenge, this paper proposes Stateful Live Video Streaming (SLVS), which is a novel self-learning approach that learns the various network features and optimizes the adaptation logic separately for different network conditions, then dynamically tunes the logic at runtime, so that bitrate decision can better match the changing networks. Moreover, we further generalize SLVS to complement the streaming platform already in service to make it compatible with any live streaming services. Extensive evaluations based on real system prototypes show that SLVS can control playback latency down to 1 s while improving Quality-of-Experience (QoE) by 17.7% to 31.8%. Moreover, it has strong robustness to maintain near-second latency over highly fluctuating networks as well as long periods of video viewing.

Adaptive video streaming has become a fundamental technology for video delivery. With the rise of deep reinforcement learning (DRL), streaming vendors are increasingly adopting DRL-driven adaptive bitrate (ABR) algorithms. In real-world deployments, most ABR approaches are developed with the aim of maintaining good performance across a wide variety of network environments. However, contrary to this expectation, our empirical findings show that even when trained on extensive real-world network trace data, these DRL-based ABR algorithms achieve only 43.1% to 48.9% of Quality-of-Experience (QoE) under highly diverse network conditions, which falls significantly short of the 100% optimum. We termed this problem as “ABR Under-Generalization”. To overcome this problem, we introduce BETA – a novel DRL-based ABR framework that incorporates both spatial and temporal learning mechanisms: 1) Spatially, BETA features a detector that flags the network conditions likely to cause poor performance, then trains specialized ABR models tailored for those conditions; 2) Temporally, BETA enhances its learning by incorporating multi-step decision experiences at each training epoch, enabling the trained model to account for long-term environmental dynamics. Comprehensive evaluations show that BETA outperforms state-of-the-art ABR algorithms, yielding average QoE gains of 19.4% to 50.9%, and achieving improvements of up to 244.1% under severely fluctuating network conditions.

Many emerging applications in edge computing require processing of huge volumes of data generated by end devices, using the freshest available information. In this paper, we address the distributed optimization of multi-user long-term average Age-of-Information (AoI) objectives in edge networks that use NOMA transmission. This poses a challenge of non-convex online optimization, which in existing work often requires either decision making in a combinatorial space or a global view of entire network states. To overcome this challenge, we propose a reinforcement learning-based framework that adopts a novel hierarchical decomposition of decision making. Specifically, we propose three different types of distributed agents to learn with respect to efficiency of AoI scheduling, fairness of AoI scheduling, as well as a high-level policy balancing these potentially conflicting design objectives. Not only does the proposed decomposition improve learning performance due to disentanglement of different design objectives/rewards, but it also enables the algorithm to learn the best policy while also learning the explanations – as actions can be directly compared in terms of the design objectives. Our evaluations show that the proposed algorithm improves the long-term average AoI by$200\%{-}300\%$and 400% compared to prior works with NOMA and the optimal solution without NOMA, respectively.

Traditional Vision-and-Language Navigation (VLN) requires an agent to navigate to a target location solely based on visual observations, guided by natural language instructions. Compared to this task, long-instruction VLN involves longer instructions, extended trajectories, and the need to consider more contextual information for global path planning. As a result, it is more challenging and requires accurately aligning the instructions with the agent’s current visual observations, which is accompanied by two significant issues. Firstly, there is a misalignment between actions. The visual observations of the agent at each step lack explicit action-related details, while the instructions contain action-oriented words. Secondly, there is a misalignment between global instructions and local visual observations. The instructions describe the entire navigation trajectory, whereas the agent’s visual observations only provide localized information about a specific position along the trajectory. To address these issues, this article introduces the Action-Perception Alignment Framework (APAF). In this framework, we first design the Action-Contextual Encoding Module (ACEM), which enriches the agent’s visual perception by encoding potential actions with relative heading and elevation angles. We then propose the Dynamic Instruction Weighting Module (DIWM), which adjusts the importance of instruction words based on the agent’s current visual observations, emphasizing those words most relevant to the agent’s visual observations. Our approach significantly outperforms existing methods, achieving state-of-the-art results with improvements of 8.5% and 4.0% in Success Rate (SR) on the long-instruction R4R and RxR datasets, respectively.

As an integration of digital twin and edge computing, the digital twin edge networks (DITENs) have been proposed in recent years to fill the gap between physical edge networks and digital systems. Meanwhile, the multi-access wireless environments in edge computing make it hard to provide ultra-reliable and low-latency communications for digital twin, especially when the jamming attacks can be launched by the adversaries. This paper studies the jamming-resilient physical-to-virtual communication (PTVC) problem in DITENs despite strong cooperative jamming. Note that the previous jamming models mainly focus on the jamming behaviors from individual adversaries and are restricted by the energy budget limitation and uniform jamming assumption. In this paper, we consider a more comprehensive jamming model, in whichfadversaries can cooperatively launch their jamming attacks in totallykwireless channels with unlimited power budget and non-uniform jamming signals. Then, based on the new proposed$(k,f)$-cooperative jamming model, we show that$k\gt f$is the necessary and also sufficient condition to solve the PTVC problem. On one hand, we prove that the PTVC problem is insoluble when$f\leq k$; on the other hand, two distributed algorithms are given as the solutions of the PTVC problem amongnphysical objectives and one sink node when$k\gt f$, the time complexity of which are$O\left ({{\frac {n \log n}{k (\log k-\log f)}}}\right)$and$O\left ({{\frac {n \log n}{\log k-\log f}}}\right)$based on the communication modes with/without acknowledgement, respectively. Both of the theoretical results and empirical simulations are conducted to show the resilience of our algorithms despite such a strong cooperative jamming model.

In this paper, we study a stable Age-of-Information (AoI) scheduling problem to handle the massive packet aggregation from arbitrary total ofnend devices to an edge device via a single hop wireless channel when information constantly arrives at the end side. Specifically, we consider the arrivals of the information with anonlineinjection mode, i.e., information is injected to end devices with an unknown injection rate in each time slot. After the information is injected, the AoI with respect to those end devices constantly increases until their fresh messages are received by the edge device, which characterizes the freshness of the information at destination. Based on the online injection mode, we propose the first distributed stable AoI scheduling algorithm combining NOMA (Non-Orthogonal Multiple-Access) technique in this paper, to minimize the expected average peak AoI (EAP-AoI) of our end-to-edge information system. Adopting NOMA technique enableskmessages decoded from a mixed signal by the edge device in our algorithm, with parameter$k\gt 1$. We prove that our algorithm is stable even under the asymptotically maximum injection rate of$O(k/n)$that any stable AoI scheduling algorithm may handle, and the EAP-AoI of our information system is bounded by$O(\sqrt [{3}]{nk})$time slots under the injection rate of$O(k/n)$. Comparing with two existing results, the EAP-AoI in our algorithm is$O\left ({{\frac {n^{2/3}}{k^{4/3}}}}\right)$and$O\left ({{\frac {n^{2/3}}{k^{1/3}}}}\right)$times smaller. Numerical results also verify the stability and efficiency of our algorithm.

In recent years, fueled by the rapid advances in high-speed mobile networks, streaming short-form videos over mobile devices (e.g., TikTok) has become ubiquitous among mobile users. Despite the widespread application, our investigation based on a real video data source revealed that a large proportion of short videos watched by viewers have suboptimal video quality (e.g., with low VMAF scores), which indicates that the Quality-of-Experience (QoE) is in fact far from optimal. This problem is primarily due to the lack of awareness of video quality optimization based on the features of the video content such as the scene complexity. To tackle this problem, this work develops a novel system called Quality Aware Short Video Streaming (QASVS), which adopts machine learning techniques to learn the video content features and then automatically generate quality-driven bitrate decision models to optimize the perceived video quality and QoE. Extensive evaluations show that QASVS is able to improve the video quality by 11.1%∼27.9% while significantly reducing the playback rebuffering compared to the state-of-the-art streaming algorithms. Therefore, QASVS is able to provide an effective way for streaming vendors to deliver high-performance short-video services.

Implementing large language models (LLMs)-driven root cause analysis (RCA) in cloud-native systems has become a key topic of modern software operations and maintenance. However, existing LLM-based approaches face three key challenges: multi-modality input constraint, context window limitation, and dynamic dependence graph. To address these issues, we propose a tool-assisted LLM agent with multi-modality observation data for fine-grained RCA, namely TAMO, including multi-modality alignment tool, root cause localization tool, and fault types classification tool. In detail, TAMO unifies multi-modal observation data into time-aligned representations for cross-modal feature consistency. Based on the unified representations, TAMO then invokes its specialized root cause localization tool and fault types classification tool for further identifying root cause and fault type underlying system context. This approach overcomes the limitations of LLMs in processing real-time raw observational data and dynamic service dependencies, guiding the model to generate repair strategies that align with system context through structured prompt design. Experiments on two benchmark datasets demonstrate that TAMO outperforms state-of-the-art (SOTA) approaches with comparable performance.

Satellite-Ground Integrated Networks (SGINs) are promising network architectures that can help reduce the load on terrestrial networks, provide mega-access capabilities and intensive task offloading functions. However, traditional resource management methods are difficult to apply directly into SGINs due to their multi-layered, heterogeneous and dynamic three-dimensional characteristics. In addition, massive multi-modal and multi-task information hinders better service performance in SGINs. Therefore, we design a multi-task integrated computation offloading model to process complex multi-modal network information, such as time-varying channel gains and dynamic Low Earth Orbit (LEO) locations, which can efficiently improve data transmission rate and privacy level. Furthermore, we propose three multi-modal based learning methods, such as centralized actor-critic (C-AC) algorithm, distributed multi-agent deep deterministic policy gradient (D-MADDPG) algorithm, and quantization-based federated learning (Q-FL) algorithm for computation-intensive, latency-critical and privacy-preserving tasks, which can further optimize the local execution or LEO offloading ratio, CPU cycle frequency and transmission power. Meanwhile, we demonstrate the quantization error upper bound between the optimal solution and the quantization scheme through massive mathematical derivations. Finally, extensive simulation results show that the proposed multi-modal based learning methods have better performance gains in terms of model convergence performance, quantization metrics, data transmission rate and number of bits processed.

Value function factorization has achieved great success in multi-agent reinforcement learning by optimizing joint action-value functions through the maximization of factorized per-agent utilities. To ensure Individual-Global-Maximum property, existing works often focus on value factorization using monotonic functions, which are known to result in restricted representation expressiveness. In this paper, we analyze the limitations of monotonic factorization and present ConcaveQ, a novel non-monotonic value function factorization approach that goes beyond monotonic mixing functions and employs neural network representations of concave mixing functions. Leveraging the concave property in factorization, an iterative action selection scheme is developed to obtain optimal joint actions during training. It is used to update agents’ local policy networks, enabling fully decentralized execution. The effectiveness of the proposed ConcaveQ is validated across scenarios involving multi-agent predator-prey environment and StarCraft II micromanagement tasks. Empirical results exhibit significant improvement of ConcaveQ over state-of-the-art multi-agent reinforcement learning approaches.

When compressing point clouds, point-based deep learning models operate points in a continuous space, which has a chance to minimize the geometric fidelity loss introduced by voxelization in preprocessing. But these methods could hardly scale to inputs with arbitrary points. Furthermore, the point cloud frames are individually compressed, failing the conventional wisdom of leveraging inter-frame similarity. In this work, we propose a patchwise compression framework called patchDPCC, which consists of a patch group generation module and a point-based compression model. Algorithms are developed to generate patches from different frames representing the same object, and more importantly, these patches are regulated to have the same number of points. We also incorporate a feature transfer module in the compression model, which refines the feature quality by exploiting the inter-frame similarity. Our model generates point-wise features for entropy coding, which guarantees the reconstruction speed. The evaluation on the MPEG 8i dataset shows that our method improves the compression ratio by 47.01% and 85.22% when compared to PCGCv2 and V-PCC with the same reconstruction quality, which is 9% and 16% better than that D-DPCC does. Our method also achieves the fastest decoding speed among the learning-based compression models.

Exploring the hierarchical structure of graphs presents notable advantages for graph analysis, revealing insights ranging from individual vertex behavior to community distribution and overall graph stability. This paper studies hierarchical structures within hypergraphs, where a hyperedge can connect multiple vertices. We observed that directly extending hierarchical frameworks from pairwise graphs to hypergraphs overlooks high-order interactions and can result in either high computational complexity or sparse hierarchy structure. To address this challenge, we introduce a dual-layer hypergraph hierarchy consisting of a primary hierarchy and a secondary hierarchy, enabling the construction of a refined hypergraph hierarchy in linear time. The dual-layer hierarchy establishes a global hierarchy based on vertex cohesion, utilizing vertex-induced subhypergraphs, and a local hierarchy based on hyperedge containment, employing edge-induced subhypergraphs. The combination of global and local hierarchy mitigates the homogeneity and sparsity issues inherent in single-layer hierarchies, allowing more effective modeling of high-order interactions. Furthermore, we propose an efficient hierarchical construction algorithm by leveraging a novel hyperedge-based disjoint set to identify connected subhypergraphs. Additionally, to optimize the local hierarchy further and prevent the emergence of excessively redundant levels, we introduce a compact local hierarchy by defining a restricted subgraph metric to eliminate redundancy caused by large-sized hyperedges. Empirical studies on real-world hypergraphs demonstrate the effectiveness of our approach.

Federated learning (FL) is a distributed machine learning approach that allows multiple devices or computing nodes to jointly train models without sharing raw data. However, in real-world application scenarios, FL usually encounters a critical challenge of data heterogeneity. Recent studies have revealed that the client’s model suffers severe bias between the local model and global model, leading to global performance degradation. Improving the generalization of local learning would inherently reduce bias. It has been proved that self-paced learning on a single device can greatly achieve a better generalization result. However, it is not well explored how it can be applied to federated learning with a number of distributed nodes working cooperatively. Specifically, self-paced learning suggests using easy data and then gradually difficult data during model training. It is not straightforward to differentiate “easy” and “difficult” data at the local since global data distribution is not available, especially with severe data heterogeneity. To address the above issues, we propose a novel federated learning framework, Federation-Paced Learning (FedPL), which enables a self-paced process in federated learning and effectively improves the model performance. First, we propose schemes to analyze the data characteristics in terms of difficulty. Then we define a stage controller to synchronize the learning process across cooperative nodes to follow the easy-to-hard rule. Finally, we propose a client selection strategy to further improve the learning efficacy. We evaluate the performance of FedPL on several generic public datasets. Experiment results show that the proposed FedPL outperforms existing methods by up to 13.50% in terms of accuracy. Code is available at https://github.com/tnghua/FedPL.

Due to the surge in mobile apps, online videos, and cloud gaming, central servers struggle with high traffic demands. Content delivery networks (CDNs) mitigate this by distributing content from edge caches, easing backbone network strain. Yet, current allocation algorithms, constrained by practical complexities and billing, often fail to optimally schedule traffic, causing server overload and increased costs. Our solution, CEQC-CDN, addresses these issues by considering DNS load balancing, regional hijacking, quality constraints, and server capacity limits. By incorporating these factors as linear constraints through binary decomposition and variable introduction, CEQC-CDN achieves global optimal traffic scheduling. Employing a greedy policy for monthly optimization, tests demonstrate an 8.57% reduction in CDN traffic costs versus conventional greedy algorithms.

Learning-based address mapping strategy has been proposed as an alternative to the conventional one-to-one mapping strategy for logical to physical address translation in flash-based Solid-State Drives (SSDs). This strategy employs a machine learning (ML)-based index to replace the memory-intensive mappings, thereby saving memory space. However, current learning-based mapping strategies mainly focus on the sequential characteristics of I/O workloads, making them less effective in scenarios with frequent updates. In this work, we introduce an access pattern-aware hybrid learning-based and conventional mapping scheme for SSDs, called APH-FTL. This approach features a machine learning-driven data classifier that employs distinct address mapping strategies for data requests with varying access patterns. Additionally, we develop a benefit-guided space regulation strategy to manage in-device memory space effectively and provide a solution to the challenge of mapping policies transformation. We evaluate APH-FTL through simulations using various real-world workloads. Experimental results demonstrate that APH-FTL significantly improves performance, reducing response time by up to 76.59% and write amplification (WA) by up to 82.97% compared with state-of-the-art schemes.

Due to its decentralized framework and outdoor environments, federated edge learning (FEEL) faces significant vulnerability to malicious attacks within edge networks. Prevailing FEEL approaches typically hinge on a dependable parameter server (PS) to contend with the adversarial updates from Byzantine clients. Recognizing the inherent unreliability of PSs in edge networks, this paper delves into the security challenges of FEEL, specifically addressing Byzantine PSs. We present a Byzantine fault-tolerant FEEL algorithm, named Fed-MS, in which a multi-server technique along with a newly designed trimmed-mean-based model filter is employed. This combination ensures that each client can obtain a feasible global model for its local training, closely approximating a true model aggregated by benign PSs. Furthermore, we propose a sparse uploading strategy in Fed-MS to enhance communication efficiency for model aggregation to multiple PSs. Theoretical analysis demonstrates that, when Byzantine PSs are a minority, Fed-MS achieves an expected convergence speed of$O(1/T)$with$T$defined as the number of training rounds, akin to state-of-the-art works under non-Byzantine settings. Extensive experiments are conducted on the CIFAR-10 dataset with MobileNet V2 as the training model. The numerical results show that our Fed-MS can improve the model accuracy from 10% to at least 76% under the malicious attacks from Byzantine PSs. Our code is released at https://github.com/haoma2772/Fed-MS.

Fixed-point decimal operations in databases with arbitrary-precision arithmetic refer to the ability to store and operate decimal fraction numbers with an arbitrary length of digits. This type of operation has become a requirement for many applications, including scientific databases, financial data processing, geometric data processing, and cryptography. However, the state-of-the-art fixed-point decimal technology either provides high performance for low-precision operations or supports arbitrary-precision arithmetic operations at low performance. In this paper, we present a design and implementation of a framework called UltraPrecise which supports arbitrary-precision arithmetic for databases on GPU, aiming to gain high performance for arbitrary-precision arithmetic operations. We build our framework based on the just-in-time compilation technique and optimize its performance via data representation design, PTX acceleration, and expression scheduling. UltraPrecise achieves comparable performance to other high-performance databases for low-precision arithmetic operations. For high-precision, we show that UltraPrecise consistently outperforms existing databases by two orders of magnitude, including workloads of RSA encryption and trigonometric function approximation.

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof systems. To address these issues, we propose FileDES ( Decentralized Encrypted Storage), which incorporates three essential elements: privacy preservation, scalable storage proof, and batch verification. FileDES provides encrypted data storage while maintaining data availability, with a scalable Proof of Encrypted Storage (PoES) algorithm that is resilient to Sybil and Generation attacks. Additionally, we introduce a rollup-based batch verification approach to simultaneously verify multiple files using publicly verifiable succinct proofs. We conducted a comparative evaluation on FileDES, Filecoin, Storj and Sia under various conditions, including a WAN composed of up to 120 geographically dispersed nodes. Our protocol outperforms the others in terms of proof generation/verification efficiency, storage costs, and scalability.

In this paper, we propose Byzantine-Robust Decentralized Federated Reinforcement Learning (BR-DeFedRL), an innovative framework that effectively combats the harmful influence of Byzantine agents by adaptively adjusting communication weights, thereby significantly enhancing the robustness of the learning system. By leveraging decentralized learning, our approach eliminates the dependence on a central server. Striking a harmonious balance between communication round count and sample complexity, BR-DeFedRL achieves efficient convergence with a rate of $\mathcal{O}\left( {\frac{1}{{TN}}} \right)$, where T denotes the communication rounds and N represents the local steps related to variance reduction. Notably, each agent attains an ϵ-approximation with a state-of-the-art sample complexity of $\mathcal{O}\left( {\frac{1}{{\varepsilon N}} + \frac{1}{\varepsilon }} \right)$. Extensive experimental validations further affirm the efficacy of BR-DeFedRL, making it a promising and practical solution for Byzantine-robust decentralized federated reinforcement learning.

QUIC is the underlying protocol of the next generation HTTP/3, serving as the major vehicle delivering video data nowadays. As a userspace protocol based on UDP, QUIC features low transmission latency and has been widely deployed by content providers. However, the high computational overhead of QUIC shifts system knobs to CPUs in high-bandwidth scenarios. When CPU resources become the constraint, HTTP/3 exhibits even lower throughput than HTTP/1.1. In this paper, we carefully analyze the performance bottleneck of QUIC and find it results from ACK processing, packet sending, and data encryption. By reducing the ACK frequency, activating UDP generic segmentation offload (GSO), and incorporating PicoTLS, a high-performance encryption library, the CPU overhead of QUIC could be effectively reduced in stable network environments. However, simply reducing the ACK frequency also impairs the transmission throughput of QUIC under poor network conditions. To solve this, we develop LiteQUIC, which involves two mechanisms towards alleviating the overhead of ACK processing in addition to GSO and PicoTLS. We evaluate LiteQUIC in the DASH-based video streaming, and the results show that LiteQUIC achieves 1.2× higher average bitrate and 93.3% lower rebuffering time than an optimized version of QUIC with GSO and PicoTLS.

To address the online learning problem in distributed systems, Streaming Federated learning (SFL) enables immediate model training by clients upon collecting new data, finding wide applications in AI-enabled Internet-of-Things and sensor networks. Given the variability in data distribution across different historical periods, the ability to recall and rapidly apply previously encountered data distributions significantly enhances the efficiency and accuracy of model training. In this paper, a demo based on the real-world temperature datasets is presented to demonstrate the importance of history knowledge in local training and the federating process of SFL, which also shows that vanilla federated learning without considering the history knowledge may even be harmful to model training. Observing this, we propose Fed-HIST, a Federated learning framework that enables the clients to learn from the HISTory knowledge of the whole distributed learning system. Unlike direct raw data storage, Fed-HIST employs model architectures to capture the data distributions, offering a more space-efficient and privacy-preserving method of knowledge storage on a server pool. Additionally, a model similarity comparison scheme is designed to retrieve beneficial knowledge from the pool uploaded by the clients in the past. Such a history-aware federation can enhance the efficiency of training each client, only requiring the recurrence of similar data distributions among SFL participants. We validate our framework through extensive simulations on MNIST, Fashion-MINST, CIFAR10, and CIFAR100 datasets, benchmarking against 9 baselines and highlighting the importance of federating from history in SFL problem through necessary ablation studies.

Due to the heterogeneous architectures and class skew, the global representation models training in resource-adaptive federated self-supervised learning face with tricky challenges: $\textit{deviated representation abilities}$ and $\textit{inconsistent representation spaces}$. In this work, we are the first to propose a multi-teacher knowledge distillation framework, namely $\textit{FedMKD}$, to learn global representations with whole class knowledge from heterogeneous clients even under extreme class skew. Firstly, the adaptive knowledge integration mechanism is designed to learn better representations from all heterogeneous models with deviated representation abilities. Then the weighted combination of the self-supervised loss and the distillation loss can support the global model to encode all classes from clients into a unified space. Besides, the global knowledge anchored alignment module can make the local representation spaces close to the global spaces, which further improves the representation abilities of local ones. Finally, extensive experiments conducted on two datasets demonstrate the effectiveness of $\textit{FedMKD}$ which outperforms state-of-the-art baselines 4.78\% under linear evaluation on average.

Over the past few years, volumetric video streaming, a cutting-edge application in virtual reality (VR) and augmented reality (AR), has gained significant attention. However, its enormous bandwidth demands exceed the capabilities of current networks to support full-size transmission. As a result, streaming vendors in the industry typically use field-of-view (FoV) prediction to reduce the streaming video size. While this approach makes transmission feasible, our measurements have shown that the adopted sequential video prefetch mechanism significantly hinders streaming performance. To address this challenge, we propose PACE, an intelligent video prefetch strategy that leverages multi-round cell-enhanced downloading. PACE divides the prefetching process for each group-of-frame (GoF) into multiple rounds, downloading different cells in each round based on periodically updated FoV prediction results. This method allows the FoV prediction to operate independently of the prefetch length limitations. Additionally, PACE incorporates a greedy-policy-based video quality decision algorithm to fully utilize network bandwidth and maximize the quality of experience (QoE). Extensive evaluations demonstrate that PACE can enhance FoV prediction accuracy by up to 23.1% and improve QoE performance by up to 54.8% while showing strong robustness across a wide range of streaming environments.

Adaptive video streaming (DASH) has become a key technology in video transmission. Given the advantages of deep re-inforcement learning (DRL), streaming vendors are increasingly focusing on DRL-based adaptive bitrate (ABR) algorithms. In practice, almost all the ABR algorithms are designed with the in-tention to work well in any size/shape of networks. However, far from expectations, our measurements revealed that, even with extensive training on a vast scale of real network trace data, if the network condition varies over a wide range, the achieved Quality-of-Experience (QoE) is only 43.1% ~ 48.9%, far below the optimal 100%. We termed this problem as “ABR Under-Generalization”. To address this problem, we developed BETA in this work, a novel ABR-specialized DRL-based approach that integrates spatial and temporal modules. 1) Spatial: BETA offers a detector that identi-fies the potential network condition that may cause poor performance and then trains complementary ABR algorithms specifically functioning in it; 2) Temporal: At each training epoch, BETA learns from the decision experience of multiple-step segments to cover long-term environmental feedback. Extensive evaluations demonstrate that BETA achieves an average QoE improvement of 19.4% to 50.9% compared to the state-of-the-art ABR algorithms, with gains of up to 244.1% in the challenging networks with dramatically variable throughput.

Short-form video streaming has seen explosive growth in recent years. Typically, the short video apps (e.g., TikTok) are installed on mobile devices, with the video content delivered over wireless networks. Our measurement study shows that almost all the state-of-the-art adaptive bitrate (ABR) algorithms for short video streaming fall short of providing satisfactory Quality-of-Experience (QoE). This is due to their reliance on bandwidth-sensitive adaptation logic with over-discretized encoding bitrate levels, which hampers their ability to manage significant wireless bandwidth fluctuations. To tackle this problem, we propose DeepReal, a fine-grained ABR approach for short videos, the core of which is a novel clustering-augmented continuous control learning algorithm. On the one hand, the clustering offers more expertise for the ABR logic training. On the other hand, the continuous-valued action domain enables the trained ABR logic to support any number of discrete bitrate levels. As a result, DeepReal's bitrate decision is able to effectively adapt to the substantial network dynamics. Large-scale evaluations show that DeepReal improves QoE by 23.5% to 33.3% while maintaining strong spatial and temporal robustness. It offers a tailored and innovative solution for current short-form video services.

A redactable blockchain allows authorized individuals to remove or replace undesirable content, offering the ability to remove illegal or unwanted information. Access control is a mechanism that limits data visibility and ensures that only authorized users can decrypt and access encrypted information, playing a crucial role in addressing privacy concerns and securing the data stored on a blockchain. Redactability and access control are both essential components when implementing a regulated consortium blockchain in real-world situations to ensure the secure sharing of data while removing undesirable content. We propose a decentralized consortium blockchain system prototype that supports redactability and access control. Through the development of a prototype blockchain system, we investigate the feasibility of combining these approaches and demonstrate that it is possible to implement a redactable blockchain with access control in a consortium blockchain setting.

The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes based on cryptographic tools like zero-knowledge proofs, as well as schemes based on trusted execution environments. In doing so, we aim to provide a condensed summary of the different approaches taken in constructing PPSC schemes. Additionally, we also offer a comparative analysis of these approaches, highlighting the similarities and differences between them. Furthermore, we shed light on the challenges that developers face when designing and implementing PPSC schemes. Finally, we delve into potential future directions for improving and advancing these schemes, discussing possible avenues for further research and development.

Wireless networks have become integral to modern communication systems, enabling the seamless exchange of information across a myriad of applications. However, the inherent characteristics of wireless channels, such as fading, interference, and openness, pose significant challenges to achieving fault-tolerant consensus within these networks. Fault-tolerant consensus, a critical aspect of distributed systems, ensures that network nodes collectively agree on a consistent value even in the presence of faulty or compromised components. This survey paper provides a comprehensive overview of fault-tolerant consensus mechanisms specifically tailored for wireless networks. We explore the diverse range of consensus protocols and techniques that have been developed to address the unique challenges of wireless environments. The paper systematically categorizes these consensus mechanisms based on their underlying principles, communication models, and fault models. It investigates how these mechanisms handle various types of faults, including communication errors, node failures, and malicious attacks. It highlights key use cases, such as sensor networks, Internet of Things applications, wireless blockchain, and vehicular networks, where fault-tolerant consensus plays a pivotal role in ensuring reliable and accurate data dissemination.

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others’ NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design.

We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is driven by the right to be forgotten and the privacy challenges in FL. We introduce a new framework for exact federated unlearning that meets two essential criteria:communication efficiencyandexact unlearning provability.To our knowledge, this is the first work to tackle both aspects coherently. We start by giving a rigorous definition ofexactfederated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data. We then pinpoint the key property that enables fast exact federated unlearning: total variation (TV) stability, which measures the sensitivity of the model parameters to slight changes in the dataset. Leveraging this insight, we develop a TV-stable FL algorithm called FATS, which modifies the classical FedAvg algorithm for TV Stability and employs local SGD with periodic averaging to lower the communication round. We also design efficient unlearning algorithms for FATS under two settings: client-level and sample-level unlearning. We provide theoretical guarantees for our learning and unlearning algorithms, proving that they achieve exact federated unlearning with reasonable convergence rates for both the original and unlearned models. We empirically validate our framework on 6 benchmark datasets, and show its superiority over state-of-the-art methods in terms of accuracy, communication cost, computation cost, and unlearning efficacy.

Betweenness centrality (BC), a classic measure which quantifies the importance of a vertex to act as a communication "bridge" between other vertices in the network, is widely used in many practical applications. With the advent of large heterogeneous information networks (HINs) which contain multiple types of vertices and edges like movie or bibliographic networks, it is essential to study BC computation on HINs. However, existing works about BC mainly focus on homogeneous networks. In this paper, we are the first to study a specific type of vertices' BC on HINs, e.g., find which vertices with typeAare important bridges to the communication between other vertices also with typeA?We advocate a meta path-based BC framework on HINs and formalize both coarse-grained and fine-grained BC (cBC and fBC) measures under the framework. We propose a generalized basic algorithm which can apply to computing not only cBC and fBC but also their variants in more complex cases. We develop several optimization strategies to speed up cBC or fBC computation by network compression and breadth-first search directed acyclic graph (BFS DAG) sharing. Experiments on several real-world HINs show the significance of cBC and fBC, and the effectiveness of our proposed optimization strategies.

With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affordable storage resources from around the world to provide robust, decentralized storage services for tens of thousands of decentralized applications (dApps). However, existing DSNs do not offer verifiability when implementing erasure coding for redundant storage, making them vulnerable to Byzantine encoders. Additionally, there is a lack of Byzantine fault-tolerant consensus for optimal resilience in DSNs. This paper introduces BFT-DSN, a Byzantine fault-tolerant decentralized storage network designed to address these challenges. BFT-DSN combines storage-weighted BFT consensus with erasure coding and incorporates homomorphic fingerprints and weighted threshold signatures for decentralized verification. The implementation of BFT-DSN demonstrates its comparable performance in terms of storage cost and latency as well as superior performance in Byzantine resilience when compared to existing industrial decentralized storage networks.

Hypergraphs are applicable to various domains such as social contagion, online groups, and protein structures due to their effective modeling of multivariate relationships. However, the increasing size of hypergraphs has led to high computation costs, necessitating efficient acceleration strategies. Existing approaches often require consideration of algorithm-specific issues, making them difficult to directly apply to arbitrary hypergraph processing tasks. In this paper, we propose a compression-array acceleration strategy involving hypergraph reordering to improve memory access efficiency, which can be applied to various hypergraph processing tasks without considering the algorithm itself. We introduce a new metric called closeness to optimize the ordering of vertices and hyperedges in the one-dimensional array representation. Moreover, we present an$\frac{1}{2w}$-approximation algorithm to obtain the optimal ordering of vertices and hyperedges. We also develop an efficient update mechanism for dynamic hypergraphs. Our extensive experiments demonstrate significant improvements in hypergraph processing performance, reduced cache misses, and reduced memory footprint. Furthermore, our method can be integrated into existing hypergraph processing frameworks, such as Hygra, to enhance their performance.

With the increment of data sets and models sizes, distributed deep learning has been proposed to accelerate training and improve the accuracy of DNN models. The parameter server framework is a popular collaborative architecture for data-parallel training, which works well for homogeneous environments by properly aggregating the computation/communication capabilities of different workers. However, in heterogeneous environments, the resources of different workers vary a lot. Some stragglers may seriously limit the whole speed, which impacts the overall training process. In this paper, we propose an adaptive multi-stage distributed deep learning training framework, named ASHL, for heterogeneous environments. First, a profiling scheme is proposed to capture the capabilities of each worker to reasonably plan the training and communication tasks on each worker, and lay the foundation for the formal training. Second, a hybrid-mode training scheme (i.e., coarse-grained and fined-grained training) is proposed to balance the model accuracy and training speed. The coarse-grained training scheme (named AHL) adopts an asynchronous communication strategy, which involves less frequent communications. Its main goal is to make the model quickly converge to a certain level. The fine-grained training stage (named SHL) uses a semi-asynchronous communication strategy and adopts a high communication frequency. Its main goal is to improve the model convergence effect. Finally, a compression-based communication scheme is proposed to further increase the communication efficiency of the training process. Our experimental results show that ASHL reduces the overall training time by more than 35% to converge to the same degree and has better generalization ability compared with state-of-the-art schemes like ADSP.

Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the performance of local models by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and prototype failure, which can limit its accuracy. In addition, it is also susceptible to poisoning attacks and server malfunction, which can degrade the quality of prototypes. To address these issues, we propose FedRFQ, a prototype-based federated learning approach that aims to reduce redundancy, minimize failure, and improve quality. FedRFQ leverages the SoftPool mechanism with prototype-based federated learning, which effectively mitigates prototype redundancy and prototype failure on Non-IID data. Moreover, we introduce the BFT-detect algorithm, a BFT detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunction. Finally, we conducted experiments on three different datasets, namely MNIST, FEMNIST, and CIFAR-10. The results demonstrate that FedRFQ outperforms existing baselines in terms of accuracy when handling Non-IID data.

Nowadays, volumetric videos have emerged as an attractive multimedia application providing highly immersive watching experiences since viewers could adjust their viewports at 6 degrees-of-freedom. However, the point cloud frames composing the video are prohibitively large, and effective compression techniques should be developed. There are two classes of compression methods. One suggests exploiting the conventional video codecs (2D-based methods) and the other proposes to compress the points in 3D space directly (3D-based methods). Though the 3D-based methods feature fast coding speeds, their compression ratios are low since the failure of leveraging inter-frame redundancy. To resolve this problem, we design a patch-wise compression framework working in the 3D space. Specifically, we search rigid moves of patches via the iterative closest point algorithm and construct a common geometric structure, which is followed by color compensation. We implement our decoder on a GPU platform so that real-time decoding and rendering are realized. We compare our method with GROOT, the state-of-the-art 3D-based compression method, and it reduces the bitrate by up to 5.98$\times$. Moreover, by trimming invisible content, our scheme achieves comparable bandwidth demand of V-PCC, the representative 2D-based method, in FoV-adaptive streaming.

Big data and strong computing power have promoted artificial intelligence to the era of big models. In particular, ChatGPT’s debut heralded the vigorous development of large models. It is an urgent problem to train large models with trillion-level parameters efficiently. Traditional single-machine training stores all data and model parameters in memory. However, due to the limitation of memory and communication resources, when the amount of data or model parameters increases, the problem of memory shortage and communication blocking often occurs. Therefore, distributed training is the most effective ways to solve the above problems and improve training efficiency. In this paper, we propose the algorithmDL-DP, which can achieve an asymptotically optimal convergence rate$O(1/{\sqrt{TK\Gamma^*}})$while satisfyingε-differential privacy, whereTis the local epoch number,Kis the global maximum iteration number and$\Gamma^*$is the minimum covering index. In particular, when${\Gamma ^*} = N$, DL-DP achieves a convergence rate of$O(1/{\sqrt{TKN}})$, which is equivalent to the best-known FedAvg approach implemented by training the full model at each client. When${\Gamma ^*} = 1$, DL-DP achieves a convergence rate of$O(1/{\sqrt{TK}})$, which is comparable to OAP that assumes all parameters need to be trained at least once in each iteration. Finally, our algorithm has been demonstrated to converge through extensive experiments.

Federated Learning (FL) offers collaborative machine learning without data exposure, but challenges arise in the mobile edge network (MEC) environment due to limited resources and dynamic conditions. This paper presents a Digital Twin (DT)-assisted FL platform for MEC networks and introduces a novel multi-FL service framework to address resource dynamics and mobile users. We leverage DT models to optimize device scheduling and MEC resource allocation, aiming to maximize utility across FL services. Our work includes heuristic and constant approximation algorithms for offline multi-FL service scenarios and we also investigate an online setting of our solution with dynamic bandwidth and moving client conditions. To adapt to changing network conditions, we utilize historical bandwidth data in DTs and implement a deep reinforcement learning algorithm, Ra_DDPG, for automatic bandwidth allocation. Evaluation results demonstrate a significant 49.8% increase in system utility compared to a benchmark algorithm, showcasing the effectiveness of our approach.

Non-volatile processors are proposed for ambient energy harvesting systems to enable accumulative computing across power failures. They employ nonvolatile memory for processor status backup before power outage and resume the system after power recovers. A straightforward backup policy is to back up all volatile data in processors, but it induces high backup cost. In this paper, we focus on branch predictor, an important component in processor, and propose efficient backup schemes to reduce backup cost while maintaining its prediction ability. We first analyze the modules in both traditional and artificial intelligence (AI) assisted designs of branch predictor, and accordingly propose three backup mechanisms pertaining to saturation-driven, locality-driven and maturity-driven backup. On the basis of these mechanisms, adaptive backup branch predictors are designed. Evaluation shows that, with traditional Tournament architecture, the proposed design achieves 15.9% and 54.1% energy reduction when compared with no-backup and all-backup strategy. For AI assisted branch predictor, the proposed design achieves 27.5% and 82.2% energy saving.

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.0% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 56.8% on average, and the average evasive rate is noticeably improved by up to 56.2%.

Federated edge learning (FEEL) is a novel paradigm that enables privacy-preserving and distributed machine learning on end devices. However, FEEL faces challenges from data/system heterogeneity among the participating clients and resource constraints of edge networks, which affect the efficiency and accuracy of the learning process. In this paper, we propose a comprehensive framework for client selection in FEEL based on the concept of Value-of-Information (VoI), which measures how valuable a client is for the global model aggregation. Our framework consists of two independent components: a VoI estimator that uses reinforcement learning to learn the relationship between VoI and various heterogeneous factors of clients; and a greedy client selector that chooses the most valuable clients under network resource constraints. Compared with most of the previous works that use concrete criteria to evaluate and select heterogeneous clients, our VoI-based approach is more comprehensive. Extensive experiments on different datasets and learning tasks are conducted, which show that our framework outperforms several state-of-the-art methods in terms of accuracy.

As an amalgamation of digital twin and edge computing, the digital twin edge networks (DITENs) have drawn much attention from industry and academia to bridge the divide between physical edge networks and digital systems. Meanwhile, the physical hardware and open-access wireless communication environments in edge networks raise significant challenges in timely information aggregation and real-time status updates for digital twins, especially in the presence of inherent failure and hostile jamming. In this paper, we investigate the fault-tolerant Age-of-Information (AoI) optimization problem for digital twin edge networks against the severe fault phenomena in wireless communications. In contrast to previous works that focus on single fault or individual jamming behavior, we propose a comprehensive communication failure model over thefnon-overlapping wireless sub-channels, wheref1out of thefsub-channels are inherently failed andf2out of thefsub-channels are jammed by adversaries. Then, based on the multi-channel communication failure model, we present a distributed fault-tolerant communication algorithm to minimize the expected average peak AoI in DITENs. Using an adaptive transmission strategy, we prove that the AoI optimization issue fornend devices can be resolved within Θ(n) time steps whenf1+f2f. Both theoretical analyses and empirical simulations are conducted to verify the fault-tolerance and efficiency of our proposed algorithm despite the communication failures.

Security early warning is crucial for resisting power system risk. To deal with renewable uncertainty, offline trained early-warning models need to be evolved incrementally. Catastrophic forgetting is main obstacle of model evolution. With the evolution times of early-warning models increasing, the capability of existing methods to mitigate catastrophic forgetting gradually decreases. Knowledge distillation can transfer the learned knowledge from the previous model to the updated model. To mitigate catastrophic forgetting, an incremental model evolution method for security early warning based on knowledge distillation and active learning is proposed. First, an incremental style-based generative adversarial network is formulated to generate renewable power scenarios, which utilizes knowledge distillation to retain previously learned knowledge. An improved concept drift detection method is proposed to determine model evolution moment. Then, an incremental deep regression model based on stacked target-related denoising autoencoder and knowledge distillation is constructed to assess system security index. Finally, active learning is deployed to select informative new samples and solve the objective conflict problem of knowledge distillation. Simulation results of a provincial grid in China demonstrate that proposed method can constantly improve early warning accuracy and effectively retain knowledge learned by previous samples. The proposed method can improve the adaptability of early-warning models to actual power system operating conditions, which enhances the intelligence level of security analysis to copy with renewable uncertainty.

This paper addresses the problem of reliable communications for cooperative learning on Internet-of-Vehicles, where a large amount of data from users and services needs to be processed. Previous works have proposed various cooperative learning schemes, but they often assume that the communications between vehicles are reliable, without considering how to achieve this in an Internet-of-Vehicles network. This paper is the first one that implements an abstract MAC layer using a distributed deep reinforcement learning scheme, which can directly meet the reliable communication requirements of cooperative learning in previous works. Our abstract MAC layer performs two operations:acknowledgement, which makes sure that all vehicles can successfully broadcast their messages to all of their neighbors, andprogress, which ensures that each vehicle can receive at least one message from its neighbors. These operations facilitate vehicles to exchange and update their training models in a cooperative learning service. Our simulation results show the efficiency and fairness of our deep reinforcement learning abstract MAC layer.

Next point-of-interest (POI) recommendation has been a hot research topic, which enables new paradigms for kinds of location-based services in real-world scenarios. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the recommendation model without sharing the highly sensitive POI data with others. However, there exist two main challenges, namelylocation noise, andbalance between personalization and knowledge sharing, seriously restrict the development of the federated next POI recommendation. To this end, in this work, we propose an adaptive clustering based personalized federated learning framework for next POI recommendation with location noise, namedCPF-POI, to address the above challenges. In detail, within the local client, a location recovery module can efficiently remove noises under the given assumption from the noisy POI data in which the recovery error bound can be theoretically proved. Then, within the parameter server, an adaptive clustering scheme is proposed to capture the internal relatedness among all clients to augment positive knowledge sharing. In order to make a balance between personalization and knowledge sharing under personalized federated learning framework, we design an alternative optimization process between clustering similar clients and minimizing local personalized loss functions. Finally, extensive experiments are conducted on two diverse real-world datasets to show the advantages ofCPF-POIover state-of-the-art methods. improvement across all metrics on average.

The robust multivariate time series anomaly detection can facilitate intelligent decisions and timely maintenance in various kinds of monitor systems. However, the robustness is highly restricted by the stochasticity in multivariate time series, which is summarized astemporal stochasticityandspatial stochasticityspecifically. In this paper, we explicitly model the temporal stochasticity variables and the latent graph relationship variables into a unified graphical framework, which can achieve better robustness to dynamicity from both the spatial and temporal perspective. First, within the spatial encoder, every connection exists or not is modeled as a binary stochastic variable, and the graph structure can be learnt automatically. Then, the temporal encoder would embed the highly structured time series into latent stochastic variables to capture both complex temporal dependencies and neighbors information. Moreover, we design a history-future combined anomaly score mechanism with both reconstruction decoder and forecasting decoder to improve the anomaly detection performance. By weighting the historical anomaly factor, the future anomaly factor, and the prediction error of current timestamp, the anomaly detection at current timestamp could be more sensitive to anomaly detection. Finally, extensive experiments on three publicly available anomaly detection datasets demonstrate our proposed method can achieve the best performance in terms of recall and F1 compared with state-of-the-arts baselines.

Deep learning (DL) powered real-time applications usually need continuous training using data streams generated over time and across different geographical locations. Enabling data offloading among computation nodes through model training is promising to mitigate the problem that devices generating large datasets may have low computation capability. However, offloading can compromise model convergence and incur communication costs, which must be balanced with the long-term cost spent on computation and model synchronization. Therefore, this paper proposes EdgeC3, a novel framework that can optimize the frequency of model aggregation and dynamic offloading for continuously generated data streams, navigating the trade-off between long-term accuracy and cost. We first provide a new error bound to capture the impacts of data dynamics that are varying over time and heterogeneous across devices, as well as quantifying varied data heterogeneity between local models and the global one. Based on the bound, we design a two-timescale online optimization framework. We periodically learn the synchronization frequency to adapt with uncertain future offloading and network changes. In the finer timescale, we manage online offloading by extending Lyapunov optimization techniques to handle an unconventional setting, where our long-term global constraint can have abruptly changed aggregation frequencies that are decided in the longer timescale. Finally, we theoretically prove the convergence of EdgeC3 by integrating the coupled effects of our two-timescale decisions, and we demonstrate its advantage through extensive experiments performing distributed DL training for different domains.

Overprivilege Attack, a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. In this paper, we propose TBAC, a Tokoin-Based Access Control model enabled by blockchain and Trusted Execution Environment (TEE) technologies, to offer fine-grained access control and strong auditability for IoT. TBAC materializes the virtual access power into a definite-amount, secure and accountable cryptographic coin, termed “tokoin” (token+coin), and manages it using atomic and accountable state-transition functions in a blockchain. A tokoin carries a fine-grained policy defined by the resource owner to specify the requirements to be satisfied before an access is granted, and the behavioral constraints that describe the correct procedure to follow during access. The strong-auditability is achieved with blockchain and a TEE-enabled trusted access control object (TACO) to ensure that all access activities are securely monitored and auditable. We prototype TBAC by implementing all its functions with well-studied cryptographic primitives over different blockchain platforms, building a TACO on top of the ARM Cortex-M33 TEE microcontroller, and constructing a user-friendly APP for regular users. A case study is finally presented to demonstrate how TBAC is employed to enable autonomous and secure in-home cargo delivery.

We investigate over-the-air federated learning (OTA-FL) that exploits over-the-air computing (AirComp) to integrate communication and computation seamlessly for FL. Privacy presents a serious obstacle for OTA-FL, as it can be compromised by maliciously manipulating channel state information (CSI). Moreover, the limited band at edge hinders OTA-FL from training large-scale models. It remains open how to enable a multitude of devices with constrained resources and sensitive data to collaboratively train a global model at band-limited edge. To tackle this, we design a novel algorithmPROBEbuilding upon a lightweight over-the-air gradients aggregation rulePB-O-GAR. Specifically,PB-O-GARcombines a random sparsification-like dimension reduction with Gaussian perturbation to provide rigorous privacy and band-adapted communication. It elaborately calibrates the transmission signal according to devices’ perceived CSI for heterogeneous power constraints accommodation and CSI attack resilience. We show that by utilizing the common randomness, which deviates from the conventional FL, random sparsification-like dimension reduction can augment privacy in addition to the intrinsic privacy amplification effect of AirComp. We establish near-optimal convergence rates and explicit trade-offs among privacy, communication and utility forPROBE. Finally, extensive experiments on benchmark datasets are conducted to validate our theoretical findings and showcase the superiority ofPROBEin realistic settings.

In this paper, we propose De-RPOTA, a novel algorithm designed for decentralized learning, equipped with mechanisms for resource adaptation and privacy protection through over-the-air computation. We theoretically analyze the combined effects of limited resources and lossy communication on decentralized learning, showing it converges towards a contraction region defined by a scaled errors version. Remarkably, De-RPOTA achieves a convergence rate of$\mathcal {O}\left ({{\frac {1}{\sqrt {nT}}}}\right)$in scenarios devoid of errors, matching the state-of-the-arts. Additionally, we tackle a power control challenge, breaking it down into transmitter and receiver sub-problems to hasten the De-RPOTA algorithm’s convergence. We also offer a quantifiable privacy assurance for our over-the-air computation methodology. Intriguingly, our findings suggest that network noise can actually strengthen the privacy of aggregated information, with over-the-air computation providing extra security for individual updates. Comprehensive experimental validation confirms De-RPOTA’s efficacy in communication resources limited environments. Specifically, the results on the CIFAR-10 dataset reveal nearly 30% reduction in communication costs compared to the state-of-the-arts, all while maintaining similar levels of learning accuracy, even under resource restrictions.

Recent years have witnessed the fast penetration of Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security and privacy issues of the VR/AR applications have been attracting considerable attention. Most VR/AR systems adopt head-mounted devices (i.e., smart headsets) to interact with users and the devices usually store the users’ private data. Hence, authentication schemes are desired for the head-mounted devices. Traditional knowledge-based authentication schemes for general personal devices have been proved vulnerable to shoulder-surfing attacks, especially considering the headsets may block the sight of the users. Although the robustness of the knowledge-based authentication can be improved by designing complicated secret codes in virtual space, this approach induces a compromise of usability. Another choice is to leverage the users’ biometrics; however, it either relies on highly advanced equipments which may not always be available in commercial headsets or introduce heavy cognitive load to users. In this paper, we propose a vibration-based authentication scheme, VibHead, for smart headsets. Since the propagation of vibration signals through human heads presents unique patterns for different individuals, VibHead employs a CNN-based model to classify registered legitimate users based the features extracted from the vibration signals. We also design a two-step authentication scheme where the above user classifiers are utilized to distinguish the legitimate user from illegitimate ones. We implement VibHead on a Microsoft HoloLens equipped with a linear motor and an IMU sensor which are commonly used in off-the-shelf personal smart devices. According to the results of our extensive experiments, with short vibration signals (≤ 1s ), VibHead has an outstanding authentication accuracy; both FAR and FRR are around 5%.

Satellite-Ground integrated networks (SGINs) are regarded as promising network architecture, which can provide global coverage, large broadband and mega access services for massive terrestrial users. Furthermore, it is beneficial to reducing network congestion, releasing network resources and achieving computation offloading functions. However, the SGIN graph structure is time-varying and highly complex, lack of fixed node orders or reference nodes, which result in dynamic multi-modal features. Hence, we consider a SGIN directed graph model to minimize the total latency while improving the model prediction accuracy, and then perform the computation offloading and quantization schemes. Specifically, we envision a spatial graph convolutional neural network framework to adapt to the dynamic SGIN graph nodes and size, and then propose a centrally deep reinforcement learning aided multi-node federated learning (CDRFL) framework to optimize the CPU cycle frequency, transmission bandwidth and the number of quantization bits to accelerate the convergence round. Extensive theoretical analyses verify the graph permutation property between SGIN graph structure and optimization problems, and demonstrate the upper bound of quantization error via massive mathematical derivation. Finally, the experimental results indicate that the proposed CDRFL framework outperforms some existing benchmarks with reference to FL convergence analysis, average latency and transmission energy consumption for all independent identically distribution (IID) and non-IID data.

Resistive random access memory (ReRAM)-based processing-in-memory (PIM) DNN accelerators have shown great potential in improving model efficiency and saving energy. To further improve memory and computation efficiency, model weight sparsity has been widely explored in ReRAM-based accelerator designs. However, these optimized accelerators rarely touched the model activation sparsity. In this paper, we observe that there exist plenty sparse rows/columns in the DNN model activation matrix which have negligible effect on accuracy, termed as insensitive rows/columns. Pruning them has little impact on model accuracy but would have significant potential to improve the performance and energy efficiency of DNN accelerators. Therefore, we propose a new ReRAM-based PIM accelerator, named as RapPIM, to take advantage of the model activation sparsity. In RapPIM, we first propose an insensitive activation rows/columns pruning method to search and prune the insensitive rows/columns. Then, we present an activation low-bits skipping strategy and a forward propagation delay hiding strategy to further improve model performance and minimize the latency of activation pruning on forward propagation. Our evaluations with several well-known DNN models show that the RapPIM achieves up to 2.40× speedup and 44.82% power reduction compared with the state-of-the-art ReRAM-based accelerator.

Vertex engagement has extraordinary significance for social resilience and network stability. There have been lots of existing work studying this fundamental problem in pairwise graphs, but in the more generalized hypergraphs, it has not been well explored, due to the great challenges of sparsity, complex connectivity and dynamicity of hypergraphs. In this work, we initialize the study of the vertex engagement problem in hypergraphs. Based on the observation that the engagement of vertices in hypergraphs needs to consider two critical parameters, group engagement and neighbor engagement, we propose a vertex engagement model integrating the merits of these two measures, called constrained core, to address the ineffectiveness and incomprehensiveness caused by just using a single engagement factor. By giving an algorithm for the constrained core decomposition, we show that the constrained core number of vertices can be computed in linear time. Furthermore, by showing a localized property of contained core, efficient maintenance algorithms for updating the constrained core number of vertices in dynamic hypergraphs are proposed, to avoid the large amount of redundant computations caused by the decomposition from scratch. Extensive experiments conducted on real-world hypergraphs well exhibit the effectiveness of our model and the efficiency of the proposed algorithms.

Due to the privacy concerns and rigorous data regulations, dynamic treatment regimes across hospitals have become increasingly difficult. Fortunately, federated learning provides a distributed learning framework to collaboratively train the model without sharing the highly sensitive electronic health record (EHR) data with others. However, there exist two main challenges, namely data quality discrepancy, and heterogeneous data distribution, which seriously restrict the development of federated dynamic treatment regimes. To this end, we develop a global data quality aware dynamic treatment regime based on hierarchical federated reinforcement learning across different hospitals. In detail, we first quantify data quality in EHR using immediate health status changes, which are then utilized as rewards to encourage the high-quality treatment actions in the offline actor-critic reinforcement learning model. Within the parameter server, an online reinforcement learning based clustering scheme is proposed to capture the internal similarities to augment the positive knowledge transfer of high-quality hospitals while neglecting the heterogeneity. Extensive experiments are conducted on two diverse real-world datasets to show the advantages of DFR-DTR over state-of-the-art baselines.

In this paper, we propose an adaptive learning paradigm for resource-constrained cross-device federated learning, in which heterogeneous local submodels with varying resources can be jointly trained to produce a global model. Different from existing studies, the submodel structures of different clients are formed by arbitrarily assigned neurons according to their local resources. Along this line, we first design a general resource-adaptive federated learning algorithm, namely RA-Fed, and rigorously prove its convergence with asymptotically optimal rate O(1/√Γ*TQ) under loose assumptions. Furthermore, to address both submodels heterogeneity and data heterogeneity challenges under non-uniform training, we come up with a new server aggregation mechanism RAM-Fed with the same theoretically proved convergence rate. Moreover, we shed light on several key factors impacting convergence, such as minimum coverage rate, data heterogeneity level, submodel induced noises. Finally, we conduct extensive experiments on two types of tasks with three widely used datasets under different experimental settings. Compared with the state-of-the-arts, our methods improve the accuracy up to 10% on average. Particularly, when submodels jointly train with 50% parameters, RAM-Fed achieves comparable accuracy to FedAvg trained with the full model.

Nowadays, volumetric video has emerged as an attractive multimedia application, which provides highly immersive watching experiences. However, streaming the volumetric video demands prohibitively high bandwidth. Thus, effectively compressing its underlying point cloud frames is essential to deploying the volumetric videos. The existing compression techniques are either 3D-based or 2D-based, but they still have drawbacks when being deployed in practice. The 2D-based methods compress the videos in an effective but slow manner, while the 3D-based methods feature high coding speeds but low compression ratios. In this paper, we propose patchVVC, a 3D-based compression framework that reaches both a high compression ratio and a real-time decoding speed. More importantly, patchVVC is designed based on point cloud patches, which makes it friendly to an field of view adaptive streaming system that further reduces the bandwidth demands. The evaluation shows patchVCC achieves the real-time decoding speed and the comparable compression ratios as the representative 2D-based scheme, V-PCC, in an FoV-adaptive streaming scenario.

In this paper, we propose a novel decentralized learning algorithm, namely DLLR-OA, for resource-constrained over-the-air computation with formal privacy guarantee. Theoretically, we characterize how the limited resources induced model-components selection error and compound communication errors jointly impact decentralized learning, making the iterates of DLLR-OA converge to a contraction region centered around a scaled version of the errors. In particular, the convergence rate of the DLLR-OA algorithm in the error-free case [EQUATION] achieves the state-of-the-arts. Besides, we formulate a power control problem and decouple it into two sub-problems of transmitter and receiver to accelerate the convergence of the DLLR-OA algorithm. Furthermore, we provide quantitative privacy guarantee for the proposed over-the-air computation approach. Interestingly, we show that network noise can indeed enhance privacy of aggregated updates while over-the-air computation can further protect individual updates. Finally, the extensive experiments demonstrate that DLLR-OA performs well in the communication resources constrained setting. In particular, numerical results on CIFAR-10 dataset shows nearly 30% communication cost reduction over state-of-the-art baselines with comparable learning accuracy even in resource constrained settings.

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.

With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.

Connected autonomous vehicles (CAVs) are promising to improve road safety, thanks to various on-board sensors, such as LiDAR, radars, and stereo cameras. However, perception view could be significantly limited due to occlusions, extreme weather, and far objects. To address these challenges, in this paper, we propose an efficient edge-assisted perception sharing scheme, which enables vehicles to exchange the information about their sensed environment to improve road safety. We formulate perception sharing as an online optimization problem, with the objective of maximizing the total weighted utility, where utility indicates the quality of collected sensor data while weight means the intensity of the vehicle's demand for information in a certain area. To solve this problem, we propose an efficient online heuristic algorithm, which decouples the original problem into multiple sub-problems and solves them alternatively to find the optimal solution. Extensive simulations demonstrate that our proposed method can significantly improve the perception sharing performance.

Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources. Decentralized Collaborative Learning (DCL) is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data. DCL has a wide range of applications in various domains, such as smart city and autonomous driving. However, DCL faces significant challenges in ensuring its trustworthiness, as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality, undermine learning reliability or violate data privacy. Therefore, it is crucial to design DCL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects: security, robustness, and privacy. We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of Trustworthy DCL (TDCL). Finally, we highlight open challenges and future directions for advancing TDCL research and practice.

The standardization of the fifth-generation (5G) communications has been completed, and the visioning and planning of the sixth-generation (6G) communications have begun, with an objective of casting the high technical standard of new spectrum, high time and phase synchronization accuracy, and 100% geographical coverage to flexibly and efficiently connect upper trillion-level devices in the future. The transition from 5G to 6G is expected to integrate all operational networks, especially the Internet of Things (IoT), which involves massive heterogeneous devices to interact with our physical world.

The study of graph kernels has been an important area of graph analysis, which is widely used to solve the similarity problems between graphs. Most of the existing graph kernels consider either local or global properties of the graph, and there are few studies on multiscale graph kernels. In this article, the authors propose a framework for graph kernels based on truss decomposition, which allows multiple graph kernels and even any graph comparison algorithms to compare graphs at different scales. The authors utilize this framework to derive variants of five graph kernels and compare them with the corresponding basic graph kernels on graph classification tasks. Experiments on a large number of benchmark datasets demonstrate the effectiveness and efficiency of the proposed framework.

Decentralized Federated Learning (DeFL) plays a critical role in improving effectiveness of training and has been proved to give great scope to the development of edge computing. However, on the one hand, inaccessibility of private data and excessively exploiting the data throughout the learning process have become a public concern, and on the other hand the connections between server-less edge devices are always varying due to the mobility of edge intelligent devices. To address the above issues, we propose aPrivacy-Enhanced -Dynamic -Decentralized -Federated -Learning algorithm called PED$ ^{2}$FL in a dynamic edge environment. We design the PED$ ^{2}$FL under the analog transmission scheme, where mobile edge devices transmit privacy preserving data simultaneously and accomplish efficient information aggregation with doubly-stochastic adjacent matrices. With thorough analysis, it can be demonstrated that PED$ ^{2}$FL satisfies$(\epsilon,\delta)$-differential privacy while the per-device privacy budget decays exponentially with the number of the neighbors, which greatly improved the data utility compared to the fixed budget in the orthogonal transmission strategy. PED$ ^{2}$FL has the same convergence rate$\mathcal {O}(\sqrt{\frac{1}{KN}})$as the non-private decentralized learning algorithm D-PSGD without enhanced privacy protection, where$K$and$N$are the total iterations and the number of nodes, respectively. Extensive experiments show that algorithm PED$ ^{2}$FL also performs well with real-world settings.

Cross-chain technology facilitates the interoperability among isolated blockchains on which users can freely communicate and transfer values. Existing cross-chain protocols suffer from the scalability problem when processing on-chain transactions. Off-chain channel, as a promising blockchain scaling technique, can enable micro-payment transactions without involving on-chain transaction settlement. However, existing channel schemes can only be applied to operations within a single blockchain, failing to support cross-chain services. Therefore in this paper, we propose$\mathsf {Cross}$-$\mathsf {Channel}$, the first off-chain channel to support cross-chain services. We introduce a novel hierarchical channel structure with a hierarchical interaction protocol, a new hierarchical settlement protocol, and a smart general fair exchange protocol, to ensure scalability, fairness, and atomicity of cross-chain interactions. Besides,$\mathsf {Cross}$-$\mathsf {Channel}$provides strong security and practicality by avoiding high latency in asynchronous networks.Through a 50-instance deployment of$\mathsf {Cross}$-$\mathsf {Channel}$on AliCloud, we demonstrate that$\mathsf {Cross}$-$\mathsf {Channel}$is well-suited for processing cross-chain transactions in high-frequency and large-scale, and brings a significantly enhanced throughput with a small amount of gas and delay overhead.

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency.

In many edge computing applications, edge devices are required to reach fault-tolerant consensus in order to provide collaborative services in outdoor environments. In this paper, we study a comprehensive$(a,b)$-majority consensus problem based on a novel failure model, which takes$a$distinct opinions as inputs and outputs a$b$-majority opinion as the final agreement. This problem formulation is drastically different from traditional ones, which usually require a majority consensus from the binary opinions of multiple supporters. It is more practical and flexible as it can accommodate more than 2 input opinions and output one that satisfies the application requirement defined by parameter$b$. We also consider physical layer in our failure model while previous models mainly focus on faults occurred in protocol layer and data layer. Based on this more realistic failure model and a more practical consensus problem definition, we present a distributed protocol for$n$edge devices to reach an$(a,b)$-majority consensus within$\Theta (n)$time steps with high probability. Empirical results from our simulation studies validate the fault tolerance property and efficiency of our work in achieving the$(a,b)$-majority consensus.

Graphs are widely employed in complex system modeling, VLSI design, and social analysis. Mining cohesive subgraphs is a fundamental problem in graph analysis, while implementing cohesive subgraphs requires analysts to not only ensure cohesiveness but also consider the computational intractability. Among a variety of diverse cohesive structures, k-truss exhibits a perfect trade-off between structure tightness and computational efficiency. In a k-truss, each edge is present in at least k-2 triangles. This study aims to contribute to this growing area of truss maintenance in fully dynamic graphs by avoiding expensive re-computation. Specifically, we consider the challenging scenario of batch processing of edge and vertex insertion/deletion and propose efficient algorithms that can maintain the trusses by only searching a very small range of affected edges. Also, our algorithms allow parallel implementations to further improve the efficiency of maintenance. Extensive experiments on both real-world static and temporal graphs illustrate the efficiency and scalability of our algorithms.

Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is a practical zero-knowledge proof system for Rank-1 Constraint Satisfaction (R1CS), enabling privacy preservation and addressing the previous scalability concerns on zero-knowledge proofs. Existing constructions of zk-SNARKs require huge memory overhead to generate proofs in that the size of the zk-SNARK circuit can be large even for a very simple use case, which limits the applications for regular resource-constrained users. To reduce the memory utilization of zk-SNARKs, this paper presents a hash-based method “Split”. Concretely, Split intends to partition the zk-SNARK circuits so that components can be processed sequentially while ensuring strong security properties leveraging hash circuits. As a zk-SNARK circuit is partitioned, obsolete variables are no longer preserved in the memory. We further propose an enhanced Split as$n$-Split, which leads to better optimization by properly choosing multiple splits. Our experimental results validate the effectiveness and efficiency of Split in conserving memory usage for resource-constrained provers as long as the circuit can be partitioned to a Good Split, indicating that via Split zk-SNARKs can be brought one step closer to practical applications.

Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously. Specifically, we first present a Byzantine-resilient distributed gradient descent algorithm that can handle the heavy-tailed data and meanwhile converge under the standard assumptions. To reduce the communication overhead, we further propose another algorithm that incorporates gradient compression techniques to save communication costs during the learning process. Theoretical analysis shows that our algorithms achieve order-optimal statistical error rate in presence of Byzantine devices. Finally, we conduct extensive experiments on both synthetic and real-world datasets to verify the efficacy of our algorithms.

Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem:how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy for the entire system?To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning system. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our approach, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.

Due to the substantial computational cost of neural network training, adopting third-party models has become increasingly popular. However, recent works demonstrate that third-party models can be poisoned. Nonetheless, most model poisoning attacks require reference data, e.g., training dataset or data belonging to the target label, making them difficult to launch in practice. In this paper, we propose a reference data independent model poisoning attack that can (1) directly search for sensitive features with respect to the target label, (2) quantify the positive and negative effects of the model parameters on sensitive features, and (3) accomplish the training of poisoned model by our parameter selective update strategy. The extensive evaluation on datasets with a few classes and numerous classes show that the attack is (I) effective: the trigger input can be labeled as a deliberate class by the poisoned model with high probability; (II) covert: the performance of the poisoned model is almost indistinguishable from the intact model on non-trigger inputs; and (III) straightforward: an adversary only needs a little background knowledge to launch the attack. Overall, the evaluation results show that our attack achieves 95%, 100%, 81%, 96%, and 96% success rates on Cifar10, Cifar100, ISIC2018, FaceScrub, and ImageNet datasets, respectively.

Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in recent years, incurring huge losses in businesses, governments, financial institutes, health providers, etc. To defeat these attacks, malware classification is employed, which plays an essential role in anti-virus products. However, existing works that employ either static analysis or dynamic analysis have major weaknesses in complicated reverse engineering and time-consuming tasks. In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy. VisMal converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family. We provided a proof-of-concept implementation and carried out an extensive evaluation to verify the performance of our framework. The evaluation results indicate that VisMal can classify a malware sample within 4.0 ms and have an average accuracy of 96.0%. Moreover, VisMal provides security engineers with a simple visualization approach to further validate its performance.

Today's business environment is characterized by uncertainty and competition, so the capability to adapt to the evolving era and unforeseen challenges is essential in business strategies. Recent studies on extended enterprise indicate that collaboration among different stakeholders is beneficial for surviving these unexpected changes. However, the barriers such as market uncertainty, privacy and trust concerns, and individual contribution evaluation limit the implementation and application of the extended enterprise concept. Federated learning (FL), in which multiple enterprise entities can use a shared model while retaining all training data locally, has emerged as a promising artificial intelligence (AI) solution for accumulating insights from multiple stakeholders and providing collaborative decision-making. Furthermore, the enhanced privacy-protection benefits of FL remove the barriers to implementing extended enterprise collaboration. In particular, an FL central server manages the local updates of multiple enterprise entities (FL clients) and aggregates their contributions to improve the global model training. Meanwhile, to address the time-series graph learning problem in most business environments, we incorporate temporal convolutional network, graph convolutional neural network, and gated recurrent unit architecture into FL to capture the temporal-spatial dependencies in individual data sources. Furthermore, we use traffic flow forecasting as the use case of our proposed framework to verify its effectiveness. Finally, the experimental results on a real traffic flow dataset and the comparison results with the state-of-the-art baseline methods show that our proposed solution achieves superior performance.

Training machine learning models in a decentralized way has attracted tremendous attention on intelligent Internet of Vehicles (IIoV). However, it is highly dynamic and asymmetric for the connections between vehicles in IIoV due to the mobility of vehicles and the complex communication environment, which poses great challenges on designing efficient distributed learning algorithms. To address this problem, we focus on the basic stochastic gradient descent (SGD) algorithm and propose a decentralized parallel SGD algorithm (DPSGD-WB) for the complex IIoV. The algorithm is based on weight-balancing to overcome the difficulty caused by the dynamic and asymmetric connectivity in IIoV. With rigorous analysis, we show that DPSGD-WB converges on the optimal rate of$O(1/\sqrt {Kn})$, where$n$is the number of vehicle terminals and$K$is the number of iterations. To the best of our knowledge, our proposed algorithm is the first known decentralized parallel SGD algorithm that can be implemented in asymmetric and dynamic intelligent IoV systems. Finally, extensive experiments demonstrate the efficacy of our algorithm.

The advancement of smart wearable devices and location-based smart services has enabled a new paradigm for smart human mobility prediction (HMP), which has a broad range of applications in smart healthcare and smart cities. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the HMP model without sharing the highly sensitive location data with others. However, in real-world scenarios, federated human mobility prediction suffers from data heterogeneity challenge, which includes two main aspects: heterogeneity mobility patterns, and data scarcity. In this paper, we propose an end-to-end federated representation learning framework for human mobility prediction, named FR-HMP, to overcome all the above obstacles. Specially, in order to enhance the representation abilities of data-scarcity clients, a two-phase learning process is proposed. The clustering module could cluster similar clients together on the parameter server to address the heterogeneous mobility patterns, and the representation learning module learns the enhanced representations of each client through the graph learning layer and graph convolution layer on the third-part server. Finally, extensive experiments are conducted using two diverse real-world HMP datasets to show the advantages of FR-HMP over state-of-the-art methods.

Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We concisely review the foundations of differential privacy and the major variants. Then, we discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, models of differential privacy in social network analysis, and a series of popular tasks, such as analyzing degree distribution, counting subgraphs and assigning weights to edges. We also discuss a series of challenges for future work.

In this article, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm.

Searching for$k$-cliques in graphs has been an important problem in graph analysis due to its large number of applications. Previously, finding$k$-cliques in weighted graphs aimed at finding cliques with the largest sum of weight (with no distinction between the edge or the vertex weights), usually called the sum model. However, the algorithms under the sum model may result in solutions consisting of low-weight vertices or edges (outliers). To address this issue, we propose a new model named maximal$(S, C, K)$-clique in weighted graphs and study the problem of maximal ($S, C, K$)-clique search (MCS). We first propose an enumeration-based algorithm MCSE, which checks every$k$-clique to identify the maximal ($S, C, K$)-clique. To improve the efficiency, we further propose two improved algorithms MCSP and MCSC. Instead of checking every possible$k$-clique, MCSP focuses on ($S, C$) values that cannot be dominated and obtains the maximal$(S, C, K)$-cliques directly based on these values. MCSC is devised by further optimizing MCSP based on some key observations on maximal cliques and cliques’ nesting property. We also propose two index structures, BCS-Index and ICS-Index, to achieve optimal query. The former stores all maximal$(S, C, K)$-cliques, while the latter uses the clique's nesting property to reduce the space cost of index construction. Extensive experiments conducted on six real graphs demonstrate the efficiency and effectiveness of our proposed algorithms.

This paper initiates the study for the basic primitive of distributed message dissemination in multi-hop wireless networks under a strong adversarial jamming model. Specifically, the message dissemination problem is to deliver a message initiating at a source node to the whole network. An efficient algorithm for message dissemination can be an important building block for solving a variety of high-level network tasks. We consider the hard non-spontaneous wakeup case, where a node only wakes up when it receives a message. Under the realistic SINR model and a strong adversarial jamming model that removes the budget constraint commonly adopted in previous work by the adversary, we present a distributed randomized algorithm that can accomplish message dissemination in$\mathscr{T}(O(D(\log n+\log R)))$time slots with a high probability performance guarantee, where$\mathscr{T}(U)$is the number of time slots in the interval from the beginning of the algorithm's execution that contains U unjammed time slots, n is the number of nodes in the network, D is the network diameter,$R$is the distance with respect to which the network is connected. Our algorithm is shown to be almost asymptotically optimal by lower bound$\Omega(D\log n)$for non-spontaneous message dissemination in networks without jamming.

We consider a$K$-armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. Although recent studies show the power of collaboration among the agents in improving the efficacy of learning, it is assumed in these studies that the communication graph should be complete or well-structured, whereas such an assumption is not always valid in practice. Furthermore, limited memorization and communication bandwidth also restrict the collaborations of the agents, since the agents memorize and communicate very few experiences. Additionally, an agent may be corrupted to share falsified experiences to its peers, while the resource limit in terms of memorization and communication may considerably restrict the reliability of the learning process. To address the above issues, we propose a three-staged collaborative learning algorithm. In each step, the agents share their latest experiences with each other through light-weight random walks in a general communication graph, and then make decisions on which arms to pull according to the recommendations received from their peers. The agents finally update their adoptions (i.e., preferences to the arms) based on the reward obtained by pulling the arms. Our theoretical analysis shows that, when there are a sufficient number of agents participating in the collaborative learning process, all the agents eventually learn the best arm with high probability, even with limited memorizing capabilities and light-weight communications. We also reveal in our theoretical analysis the upper bound on the number of corrupted agents our algorithm can tolerate. The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.

Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to place redundant SFC backups at the edge for quickly recovering from any failures. The existing research largely overlooks the influences of SFC popularity, backup completeness, and failure rate on the optimal deployment of SFC backups on edge servers. In this paper, we comprehensively consider from the perspectives of both the end users and edge system to backup SFCs for providing popular services with the lowest latency. To overcome the challenges resulted from unknown SFC popularity and failure rate, as well as the known system parameter constraints, we take advantage of the online bandit learning technique to cope with the uncertainty issue. Combining the Prim -inspired method with the greedy strategy, we propose a Real-Time Selection and Deployment (RTSD) algorithm. Extensive simulation experiments are conducted to demonstrate the superiority of our proposed algorithms.

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge computing environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this article, we study energy-aware DNN model training in edge computing. We first formulate a novel energy-aware, Device-to-Device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and energy capacity on each IoT device. We then devise a near-optimal learning algorithm for the problem when the training data follows the i.i.d. data distribution. The crux of the proposed algorithm is to explore using the energy of neighboring devices of each device for its local model uploading, by reducing the problem to a series of weighted maximum matching problems in corresponding auxiliary graphs. We also consider the problem without the assumption of the i.i.d. data distribution, for which we propose an efficient heuristic algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising.

The proliferation of edge computing brings new challenges due to the complexity of decentralized edge networks. Software-defined networking (SDN) takes advantage of pro-grammability and flexibility in handling complicated networks. However, it remains a problem of designing a both trusted and scalable SDN control plane, which is the core component of the SDN architecture for edge computing. In this paper, we propose Curb, a novel group-based SDN control plane that seamlessly integrates blockchain and BFT consensus to ensure byzantine fault tolerance, verifiability, traceability, and scalability within one framework. Curb supports trusted flow rule updates and adaptive controller reassignment. Importantly, we leverage a group-based control plane to realize a scalable network where the message complexity of each round is upper bounded by O(N), where N is the number of controllers, to reduce overheads caused by blockchain consensus. Finally, we conduct extensive simulations on the classical Internet2 network to validate our design.

Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up public balances instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.

This paper studies the privacy-preserving adaptive ranking problem for multi-armed-bandit crowdsourcing, where according to the crowdsourced data, the arms are required to be ranked with a tunable granularity by the untrustworthy third-party platform. Any online worker can provide its data by arm pulls but requires its privacy preserved, which will increase the ranking cost greatly. To improve the quality of the ranking service, we propose a Privacy- Preserving Adaptive Ranking algorithm called PPAR, which can solve the problem with a high probability while differential privacy can be ensured. The total cost of the proposed algorithm is ${\mathcal{O}}(K\ln K)$, which is near optimal compared with the trivial lower bound Ω(K), where K is the number of arms. Our proposed algorithm can also be used to solve the well-studied fully ranking problem and the best arm identification problem, by proper setting the granularity parameter. For the fully ranking problem, PPAR attains the same order of computation complexity with the best-known results without privacy preservation. The efficacy of our algorithm is also verified by extensive experiments on public datasets.

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper we study energy-aware DNN model training in an edge environment. We first formulate a novel energy-aware, device-to-device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and the energy capacity on each IoT device. We then devise an efficient heuristic algorithm for the problem. The crux of the proposed algorithm is to explore the energy usage of neighboring devices of each device for its local model uploading, by reducing the problem to a series of maximum weight matching problems in corresponding auxiliary graphs. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.

To realize simulation experiments in large-scale Internet of Things (IoT) networks, this work studies the utilization of deep graph generative models to generate IoT networks, which can provide an economic approach facilitating IoT to meet the requirements of real-time performance, interoperability, energy efficiency, and coexistence. In IoT, nodes have different attributes, different connection ways with surrounding nodes, and different compactness of the region, which pose great challenges for network generation. By leveraging the properties of$k$-core and variational autoencoder during network generation, we propose a variable graph autoencoder called Core-GAE incorporating the coreness of nodes. In contrast to previous graph generative models, Core-GAE can preserve the local proximity similarity and maintain the global structural features simultaneously when learning the structural features of graphs. All three of the tasks we experimented with on four data sets show that Core-GAE exhibits better performance than previous ones.

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain.

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain -- CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain.

This article studies the core maintenance problem in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which is widely used in large-scale graph analytics. The core maintenance problem requires updating the core numbers of vertices after a set of edges and vertices are inserted into or deleted from the graph. Previous works focus on the scenario of single-edge updates and process the edges one by one when multiple edges are inserted/deleted. We initiate the studies of processing multiple edges concurrently to improve the efficiency of core maintenance. Specifically, we discover a structure of inserted/deleted edges,superior edge set, which can be processed together to greatly reduce unnecessarily repeated visits of vertices in the procedure of sequential edge processing. Based on the structure of the superior edge set, efficient algorithms are then devised for incremental and decremental core maintenance, respectively. Compared with single-edge processing algorithms, our algorithms show a significant speedup in the processing time. Furthermore, our algorithms admit parallel implementations, which can further improve the update efficiency. We also conduct extensive experiments on different types of real-world, temporal, and synthetic data sets, and the results illustrate that the proposed algorithms exhibit good efficiency, stability, and scalability.

This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a decentralized wireless federated learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies$(\epsilon,\delta)$-differential privacy and the privacy budget per worker scales as$\mathcal {O}(\frac{1}{\sqrt{N}})$, in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of$\mathcal {O}(\sqrt{\frac{1}{TN}})$as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.

Nowadays, with the development of various kinds of sensors in smartphones or wearable devices, human activity recognition (HAR) has been widely researched and has numerous applications in healthcare, smart city, etc. Many techniques based on hand-crafted feature engineering or deep neural network have been proposed for sensor based HAR. However, these existing methods usually recognize activities offline, which means the whole data should be collected before training, occupying large-capacity storage space. Moreover, once the offline model training finished, the trained model can't recognize new activities unless retraining from the start, thus with a high cost of time and space. In this paper, we propose a multi-modality incremental learning model, called HarMI, with continuous learning ability. The proposed HarMI model can start training quickly with little storage space and easily learn new activities without storing previous training data. In detail, we first adopt attention mechanism to align heterogeneous sensor data with different frequencies. In addition, to overcome catastrophic forgetting in incremental learning, HarMI utilizes the elastic weight consolidation and canonical correlation analysis from a multi-modality perspective. Extensive experiments based on two public datasets demonstrate that HarMI can achieve a superior performance compared with several state-of-the-arts.

Energy-efficient communication technologies are a key enabler for many IoT applications. Many existing communication protocols are based on duty-cycling techniques, that have an inherent tradeoff between delay and energy consumption. In the field of sensor networks, wake-up receivers have been investigated to overcome these problems and to further reduce energy consumption. We now go one step further and investigate the use of wake-up receivers in combination with IEEE 802.11 WLAN. We extend the protocol used to communicate between the access point and the client to introduce a wake-up signal. This can be implemented in a way that is fully compatible with existing wireless LAN (WLAN) standards, thus, it can be deployed gradually with little effort and no need to change existing systems. As a proof of concept and to perform first lab experiments, we developed a hardware prototype using a selective wake-up receiver and off-the-shelf USB-WLAN dongles. All experimental results are verified using an analytical model and a detailed simulation study. We show that our wake-up WLAN can provide connectivity for low-power devices with low delays and low energy consumption at the same time.

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by conventional Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected cumulative sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers is massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments.

The emergence of mobile edge computing (MEC) empowers the popularity of resource-consuming applications on edge devices, where virtual network functions (VNFs) are widely employed to provide flexible and scalable network service to users in an individual or chained manner. To maintain high availability of VNFs, deploying redundant backups of VNFs on edge servers has become an effective method for swift recovery. However, the existing studies largely ignore the impact of heterogeneous VNF importance on appropriately placing VNF backups at the edge. In this work, we specify the concept of VNF demand levels considering both the user service needs and service function chain (SFC) composition requirements to fill the above gap, which is employed as a significant factor to formulate the edge-assisted VNF backup issue as an optimization problem with the resource constraint of edge servers. To solve the challenge brought by the uncertain demand levels of VNFs, we resort to the combinatorial multi-armed bandit (CMAB) problem to propose an online learning based approximate VNF backup selection and deployment algorithm, named BSPS, with acceptable computation complexity and regret bound. Furthermore, regarding the balance of workload among edge servers, the basic problem is extended to accommodate the extra constraint and an additional VNF backup scheme is devised accordingly. Extensive simulation results with numerical analysis demonstrate the attractive performances of our proposed schemes.

In this paper, we study exact hypercore maintenance in large-scale dynamic hypergraphs. A hypergraph, whose hyperedges may contain a set of vertices rather than two vertices in pairwise graphs, can represent complex interactions in more sophisticated applications. However, the exponential number of hyperedges incurs unaffordable costs to recompute the hypercore number of vertices and hyperedges when updating a hypergraph. This motivates us to propose an efficient approach for exact hypercore maintenance with the intention of significantly reducing the hypercore updating time comparing with recomputation approaches. The proposed algorithms can pinpoint the vertices and hyperedges whose hypercore numbers have to be updated by only traversing a small sub-hypergraph. Extensive experiments on real-world and temporal hypergraphs demonstrate the superiority of our algorithms in terms of efficiency.

Scalability has long been a major challenge of cryptocurrency systems, which is mainly caused by the delay in reaching consensus when processing transactions on-chain. As an effective mitigation approach, the payment channel networks (PCN) enable private channels among blockchain nodes to process transactions off-chain, relieving long-time waiting for the online transaction confirmation. The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing, scheduling, and initial deposits, as well as preventing the system from security and privacy attacks. However, the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected. To fill this gap, we employ game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks. Our study is progressive, as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN. Our analysis is complementary, since we utilize not only the classic game theory with the complete rationality assumption, but also the evolutionary game theory considering the limited rationality of players to portray the evolution of the PCN. The results of numerous simulation experiments verify the effectiveness of our analysis.

This article studies privacy-preserving collaborative learning in decentralized Internet-of-Things (IoT) networks, where the agents exchange information constantly to improve the learnability, and meanwhile make the privacy of agents protected during communications. However, the harsh constraints in IoT make executing collaborative learning much more difficult than well-connected systems composed by servers with strong computation power, due to the weak capacity of devices, limited bandwidth for exchanging information, the asynchronous communication environment, and the necessity of privacy preserving. We show that even if with the harsh constraints in IoT, it still can devise efficient privacy-preserving collaborative learning algorithms, by proposing the first known decentralized collaborative learning algorithm for the fundamental multiarmed bandits problem under the framework of local differential privacy. Rigorous analysis shows that the proposed learning algorithm can make every agent learn the best arm with a high probability and keep the privacy preserved meanwhile. Extensive experiments illustrate that our learning algorithm performs well in real settings.

Multiobject tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this article, spatial-temporal co-occurrence constraints (STCCs) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking. It inherits the advantages of the blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In the edge computing environment, it achieves real-time performance on cross-camera tracking. On the MOT17 benchmark, our method shows the state-of-the-art results compared with other online trackers.

In the past decades, Dynamic Internet of Things (D-IoT) networks have played a conspicuously more important role in many real-life areas, including disaster relief, environment monitoring, public safety, and so on, to rapidly collect information from the environment and help people to make the decision. Meanwhile, due to the widespread implementation of dynamic IoT networks, there exists an enormous demand on designing suitable models and efficient algorithms for fundamental operations in dynamic IoT networks, to achieve the high throughput and reliable low-latency communication demands in 6G networks. In this article, we first present a general dynamic model to comprehensively depict most of the dynamic phenomena in IoT networks. Then, based on the proposed dynamic model, a distributed scheduling algorithm is proposed to competitively optimize the Age-of-Information (AoI) problem in the context of a D-IoT network. We say our scheduling algorithm is competitive: the throughput of the base station approximates the optimal solution with constant competitive ratio; and, the latency for a packet received by the base station is only constant times larger than the optimal latency. Rigorous theoretical analysis and extensive simulations are presented to verify the high throughput and reliable low-latency communications in our proposed algorithm.

Dynamicity is one of the most challenging, yet, key aspects of wireless networks. It can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. In this work, we propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model makes use of the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted by most existing research. Under the proposed dynamic model, we develop an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhnet al.[24]. Our solution paves the way for many new fast algorithms to solve high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulation studies indicate that our algorithm exhibits good performance in realistic environments with dynamic network behaviors.

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of$O(\ln n)$, where$n$is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of$\Theta (\ln n)$. Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule.

In this paper, we investigate distributed broadcasting in dynamic networks, where the topology changes continually over time. We propose a network model that captures the dynamicity caused by both churn and mobility of nodes. In contrast to existing work on dynamic networks, our model defines the dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a local dynamic model suits distributed algorithms better than a global one. The proposed dynamic model uses the more realistic SINR model to depict wireless interference, instead of oversimplified graph-based models adopted in most existing work. We consider the fundamental communication primitive of global broadcast, which is to disseminate a message from a source node to the whole network. Specifically, we present a randomized distributed algorithm that can accomplish dynamic broadcasting in an asymptotically optimal running time of$O(D_{T})$with a high probability guarantee, under the assumption of reasonably constantdynamicity rate, where$D_{T}$is thedynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. We believe our local dynamic model can greatly facilitate distributed algorithm studies in mobile and dynamic wireless networks.

In this paper, we present the first algorithm to precisely implement the abstract MAC (absMAC) layer under the physical SINR model in dynamic networks. The absMac layer, first presented by (Kuhn et al., 2009), provides reliable local broadcast communications, with timing guarantees stated in terms of a collection of abstract delay functions, based on which high-level algorithms can be designed, independent of specific channel behaviors. The implementation of absMAC requires the design of a distributed algorithm for the local broadcast communication primitives over a particular communication model that defines concrete channel behaviors, and the objective is to minimize the bounds of the abstract delay functions. Halldórsson et al. (2015) showed that under the standard SINR model (synchronous communications without physical carrier sensing or location information), there exist no efficient exact implementations. In this work, we demonstrate that physical carrier sensing, a commonly seen function performed by wireless devices, can help get efficient exact implementation algorithms. Specifically, we propose an algorithm that precisely implements the absMAC layer under the SINR model in dynamic networks. The algorithm provides asymptotically optimal bounds for both acknowledgement and progress functions defined in the absMAC layer. Our algorithm leads to many new faster algorithms for solving high-level problems under the SINR model in dynamic networks. We demonstrate this by exemplifying problems of Consensus, Multi-Message Broadcast, and Single-Message Broadcast. It deserves to point out that our implementation algorithm is designed based on an optimal algorithm for a General Local Broadcast (GLB) problem, which takes the number of distinct messages into consideration for the first time. The GLB algorithm can handle many communication scenarios apart from those defined in the absMAC layer. Simulation results show that our proposed algorithms perform well in reality.

The byzantine model is widely used to depict a variety of node faults in networks. Previous studies on byzantine-resilient protocols in wireless networks assume reliable communications and do not consider the jamming behavior of byzantine nodes. Such jamming, however, is a very critical and realistic behavior to be considered in modern wireless networks. In this paper, for the first time, we integrate the jamming behavior of byzantine nodes into the network setting. We show that, in this much more comprehensive and harsh model, efficient distributed communication protocols can be still devised with elaborate protocol design. In particular, we developed an algorithm that can accomplish the basic multiple-message dissemination task close to the optimal solution in terms of running time. Empirical results validate the byzantine-resilience and efficiency of our algorithm.

Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands for processing various contents (e.g., videos). Generally, for efficient service delivery, the contents from the service providers are processed on the edge servers (ESs), as edge computing offers vehicular applications low-latency services. However, due to the reusability of the same contents required by different distributed vehicular users, processing the copies of the same contents repeatedly in an edge server leads to a waste of resources (e.g., storage, computation, and bandwidth) in ESs. Therefore, it is a challenge to provide high-quality services while guaranteeing the resource efficiency with edge content caching. To address the challenge, an edge content caching method for smart cities with service requirement prediction, named E-Cache, is proposed. First, the future service requirements from the vehicles are predicted based on the deep spatiotemporal residual network (ST-ResNet). Then, preliminary content caching schemes are elaborated based on the predicted service requirements, which are further adjusted by a many-objective optimization aiming at minimizing the execution time and the energy consumption of the vehicular services. Eventually, experimental evaluations prove the efficiency and effectiveness of E-Cache with spatiotemporal traffic trajectory big data.

In this paper, we study distributed algorithms to realize efficient communications under the Rayleigh-fading model. This model extends the popular deterministic SINR model using stochastic propagations to address the fading effects observed in reality. Stochastic propagations can greatly increase the difficulty of handling interference and collisions, especially in a local context without much global knowledge. We present a new technique called Inductive Coloring that can be used to schedule fast transmissions with Rayleigh-fading interference. The computation of inductive coloring takes only O(log2n) time with the proposed distributed algorithm, where n is the number of nodes in the network. We illustrate the power of inductive coloring by giving a distributed and randomized algorithm to implement the abstract MAC (absMAC) layer, which was first proposed by Kuhn et al.. With two basic time-guaranteed communication primitives, namely acknowledgement and progress, which correspond to the operations of node local broadcasts and message receptions from others, the absMAC layer decomposes the algorithm design and analysis in networks into two independent components, i.e., implementing the absMAC layer over a physical network and designing algorithms with the help of the two primitives in the absMAC layer. Thus, it sharply reduces the fussy and complicated process of algorithm design and analysis over the physical network. Our proposed algorithm implements the absMAC layer under the Rayleigh-fading model with no more than a logarithmic factor inferior to the optimal solution in terms of time complexity. The presented simulation results indicate that our algorithm performs well in realistic environments. Furthermore, we show that by making full use of our proposed absMAC layer algorithm, many network primitives such as Neighbor Discovery, Single/Multiple-Message Broadcast, and Consensus, can be efficiently implemented.

Decentralized machine learning has been playing an essential role in improving training efficiency. It has been applied in many real‐world scenarios, such as edge computing and IoT. However, in fact, networks are dynamic, and there is a risk of information leaking during the communication process. To address this problem, we propose a decentralized parallel stochastic gradient descent algorithm (D‐(DP)2SGD) with differential privacy in dynamic networks. With rigorous analysis, we show that D‐(DP)2SGD converges with a rate of while satisfying ε‐DP, which achieves almost the same convergence rate as previous works without privacy concern. To the best of our knowledge, our algorithm is the first known decentralized parallel SGD algorithm that can implement in dynamic networks and take privacy‐preserving into consideration.

Age of Information (AoI) is an emerging concept to model information freshness from the perspective of destinations of information deliveries. Serving as a metric to characterize the data delivery timeliness, the peak age indicates the maximum value of the AoI prior to a data packet reception. In this paper, we present a distributed scheduling algorithm for peak age optimization in a wireless network where a number of sensor nodes attempt to deliver their sensed data to a data collector over a wireless channel. In particular, each sensor node accesses the channel for data delivery independently according to an adaptively tuned transmission probability. The beauty of our algorithm lies in that, even with neither centralized infrastructure nor coordinations among the sensor nodes, our algorithm asymptotically approximates the optimal solution by only a constant factor. We perform solid theoretical analysis and extensive simulations to verify the efficacy of our algorithm. To the best of our knowledge, it is the first fully distributed scheduling algorithm for AoI optimization in wireless networks.

With distinct privacy protection advantages, federated recommendation is becoming increasingly feasible to store data locally in devices and federally train recommender models. However, previous work on federated recommender systems does not take full account of the limitations of storage, RAM, energy and communication bandwidth in the mobile environment. Their model scales are too big to run easily in mobile devices. Moreover, existing federated recommenders need to fine-tune recommendation models in each device, which makes them hard to effectively exploit collaborative filtering (CF) information among users/devices.

Person reidentification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of this task is the hard sample mining. Technically, two strategies could be employed, i.e., global hard mining and local hard mining. For the former, hard samples are mined within the entire training set, while for the latter, it is done in mini-batches. In literature, most existing methods operate locally. Examples include batch-hard sample mining and semihard sample mining. The reason for the rare use of global hard mining is the high computational complexity. In this article, we argue that global mining helps to find harder samples that benefit model training. To this end, this article introduces a new system to: 1) efficiently mine hard samples (positive and negative) from the entire training set and 2) effectively use them in training. Specifically, a ranking list network coupled with a multiplet loss is proposed. On the one hand, the multiplet loss makes the ranking list progressively created to avoid the time-consuming initialization. On the other hand, the multiplet loss aims to make effective use of the hard and easy samples during training. In addition, the ranking list makes it possible to globally and effectively mine hard positive and negative samples. In the experiments, we explore the performance of the global and local sample mining methods, and the effects of the semihard, the hardest, and the randomly selected samples. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.

This article studies the batch processing of truss maintenance in large graphs. Trussness is a widely used index in graph analytics for cohesive subgraph mining. It is defined on edges to reflect the closeness of vertices connected by the edges. The trussness maintenance problem, i.e., updating trussness after edge insertions/deletions and avoiding recomputation, was proposed by Cohen (2008) with the assumption that real graphs are continuously evolving in nature and their dynamicity usually only affects few edges. Different from the existing work that mainly focuses on simple single edge/vertex insertion/deletion, we propose batch processing algorithms for truss maintenance with multiedge insertions/deletions. By presenting an edge structure called triangle disjoint set, whose insertion/deletion can make each edge change its trussness by at most 1, we tackle the difficulty in quantifying the trussness changes of the edges in batch processing. More specifically, we propose two indices, namely sustain support and pivotal support, to help measure whether the edges have the potential to change their trussness, such that the search range of the potential edges is greatly reduced. The extensive experiments on real-world graphs illustrate that compared with the single-edge processing approach, our batch processing algorithms can significantly improve the processing efficiency, and the improvement becomes more obvious when more edges are inserted/deleted.

This article studies the core maintenance problem for dynamic graphs which requires to update each vertex's core number with the insertion/deletion of vertices/edges. Previous algorithms can either process one edge associated with a vertex in each iteration or can only process one superior edge associated with the vertex (an edge 〈u; v〉 is a superior edge of vertex u if v' core number is no less than u's core number) in each iteration. Thus for high superior-degree vertices (the vertices associated with many superior edges) insertions/deletions, previous algorithms become very inefficient. In this article, we discovered a new structure called joint edge set whose insertions/deletions make each vertex's core number change at most one. The joint edge set mainly contains all the superior edges associated with the high superior-degree vertices as long as these vertices are 3+-hop independent. Based on this discovery, faster parallel algorithms are devised to solve the core maintenance problems. In our algorithms, we can process all edges in the joint edge set in one iteration and thus can greatly increase the parallelism and reduce the processing time. The results of extensive experiments conducted on various types of real-world, temporal, and synthetic graphs illustrate that the proposed algorithms achieve good efficiency, stability and scalability. Specifically, the new algorithms can outperform the single-edge processing algorithms by up to four orders of magnitude. Compared with the matching based algorithm and the superior edge based algorithm, our algorithms show a significant speedup up to 60× in the processing time.

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity$O(n\ln n)$, where$n$is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of$O(\ln n)$is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

We study the fundamental problem of fault-tolerant distributed sampling towards uniform probabilistic distribution in dynamic multi-hop wireless networks. Whereas uniform sampling has been extensively studied without concerning fault tolerance, only quite few proposals investigate how the uniform sampling algorithm tolerate Byzantine faults on dynamic networks with very special topologies, e.g., regular graphs with constant node degree. Therefore, designing fault-tolerant uniform sampling algorithms for more general graphs is still an open problem. To this end, we propose a fast and highly fault-tolerate randomized algorithm, such that nearly-uniform sampling is achieved in O(log2n) rounds, while up to O(√n/(polylog(n)·Δ)) Byzantine nodes can be tolerated, where Δ is the maximum degree of the network. Moreover, the proposed algorithm is also communication efficient in the sense that only O(log n) bits need to be exchanged on each link in every round. To show the power of distributed uniform sampling, we apply the proposed algorithm in designing polylogarithmic time distributed algorithms for two typical fundamental issues, i.e., to achieve agreement or data aggregation in Byzantine dynamic networks.

This paper studies jamming-resilient distributed backbone construction in multi-hop wireless networks. Specifically, a strong adversarial jamming model is proposed that captures the general jamming phenomena suffered by wireless communications. The jamming model is based on the realistic Signal-to-Interference-plus-Noise-Ratio (SINR) interference model, and is featured by local-uniformity, unrestricted energy budget and reactivity, which covers more jamming scenarios and is much closer to reality than existing jamming models. Under the strong adversarial jamming model, we propose a randomized distributed algorithm that can construct a backbone in J(O(log n + logR)) rounds with high probability, where J(O(log n + log R)) is the number of rounds in the interval from the beginning of the algorithm execution that contains O(log n + log R) unjammed rounds for every node. This result is asymptotically optimal considering the trivial lower bound of Ω(log n) for a successful transmission even without interference and jamming.

This paper investigates distributed Dominating Set (DS) and Connected Dominating Set (CDS) construction in dynamic wireless networks under the SINR interference model. Specifically, we present a new model for dynamic networks that admits both churns (due to node arrivals/departures) and node mobility. Under this dynamic model, we propose efficient algorithms to construct a DS and a CDS with constant approximation ratios w.r.t. the corresponding minimum ones in O(log n) time with a high probability guarantee. To the best of our knowledge, these algorithms are the first known ones for DS and CDS construction in dynamic networks assuming the SINR interference model. We believe our dynamic network model can greatly facilitate distributed algorithm studies in mobile and dynamic wireless networks.

Most existing proposals for indoor localization are “unnatural,” as they rely on sensing abilities not available to human beings. While such a mismatch causes complications in human-computer interactions and thus potentially reduces the usability and friendliness of a localization service, it is partially entailed by the need for low-cost/effort sensing with resource-limited mobile devices. Fortunately, recent developments in smart glasses (e.g., Google Glasses) signal a trend toward realistic visual sensing and hence make the sensing ability of mobile devices more compatible to that of human users. Leveraging such front-end developments, we propose VisioMap as a natural indoor localization system that intentionally mimics the human skills in visual localization. VisioMap uses very sparse photograph samples to reconstruct 3-D indoor scenes; this is facilitated by the facts that photographs are taken at the eye-level with high stability and regularity, and that the reconstruction is lightweight as it exploits geometric features rather than image pixels. Localization is in turn performed by matching the geometric features extracted online to the reconstructed 3-D scene, making VisioMap: 1) natural to users as they can see the matched 3-D scene and 2) dispensed with the need for dense fingerprints/POIs toward accurate localization.

Graph partitioning is a fundamental problem to enable scalable graph computation on large graphs. Existing partitioning models are either streaming based or offline based. In the streaming model, the current edge needs all previous edges' partition choices to make a decision. As a result, it is hard to carry out partitioning in parallel. Besides, offline based partitioning requires full knowledge about the input graph which may not suit well for large graphs. In this work, we propose a quasi-streaming partitioning model and a game theory based solution for the edge partitioning problem. Specifically, we separate the whole edge stream into a series of batches where the batch size is a constant multiple of the number of partitions. In each batch, we model the graph edge partitioning problem as a game process, where the edge's partition choice is regarded as a rational strategy choice of the player in the game. As a result, the edge partitioning problem is decomposed into finding Nash Equilibriums in a series of game processes. We mathematically prove the existence of Nash Equilibrium in such a game process, and analyze the number of rounds needed to converge into a Nash Equilibrium. We further measure the quality of these Nash Equilibriums via computing the PoA (Price of Anarchy), which is bounded by the number of partitions. Then we evaluate the performance of our strategy via comprehensive experiments on both real-world graphs and random graphs. Results show that our solution achieves significant improvements on load balance and replication factor when compared with five exsiting streaming partitioning strategies.

Privacy-preserving data aggregation has been extensively studied in the past decades. However, most of these works target at specific aggregation functions such as additive or multiplicative aggregation functions. Meanwhile, they assume there exists a trusted authority which facilitates the keys and other information distribution. In this paper, we aim to devise a communication efficient and privacy-preserving protocol that can exactly compute arbitrary data aggregation functions without trusted authority. In our model, there exist one untrusted aggregator and n participants. We assume that all communication channels are insecure and are subject to eavesdropping attacks. Our protocol is designed under the semi-honest model, and it can also tolerate k (k ≤ n-2) collusive adversaries. Our protocol achieves (n - k) -source anonymity. That is, for the source of each collected data aparting from the colluded participants, what the aggregator learns is only from one of the (n - k) non-colluded ones. Compared with recent work [1] that computes arbitrary aggregation functions by collecting all the participants' data using the trusted authority, our protocol increases merely by at most a factor of O(([logn/loglogn])2) in terms of computation time and communication cost. The key of our protocol is that we have designed algorithms that can efficiently assign unique sequence numbers to each participant without the trusted authority.

In this paper, we present the first algorithm for exactly implementing the abstract MAC (absMAC) layer in the physical SINR model. The absMac layer, first presented by Kuhn et al. in [15], provides reliable local broadcast communication, with timing guarantees stated in terms of a collection of abstract delay functions, such that high-level algorithms can be designed in terms of these functions, independent of specific channel behavior. The implementation of absMAC layer is to design a distributed algorithm for the local broadcast communication primitives over a particular communication model that defines concrete channel behaviors, and the objective is minimizing the bounds of the abstract delay functions. Halldórsson et al. [10] have shown that in the standard SINR model (synchronous communication, without physical carrier sensing or location information), there cannot be efficient exact implementations. In this work, we show that physical carrier sensing, a commonly seen function performed by wireless devices, can help get efficient exact implementation algorithms. Specifically, we propose an algorithm that exactly implements the absMAC layer. The algorithm provides asymptotically optimal bounds for both acknowledgement and progress functions defined in the absMAC layer. Our algorithm can lead to many new faster algorithms for solving high-level problems in the SINR model. We demonstrate this by giving algorithms for problems of Consensus, Multi-Message Broadcast and Single-Message Broadcast. It deserves to point out that our implementation algorithm is designed based on an optimal algorithm for a General Local Broadcast (GLB) problem, which takes the number of distinct messages into consideration for the first time. The GLB algorithm can handle much more communication scenarios apart from those defined in the absMAC layer. Simulation results show that our proposed algorithms perform well in reality.

Dynamicity is one of the critical characteristics and a major challenge in designing communication protocols in wireless networks. Most of the previous works had focused on the internal node changes (e.g., mobility, arrival, or departure) and not considered the effect of external environmental change. However, the external environmental change, in general, is a more complex phenomenon that can impede nodes from successful communication, implying the protocols of the previous dynamic models do not work well in practice. In this paper, we give an algorithm for distributed broadcasting in a more general model with fully dynamic wireless networks, called FD-Broadcast. Specifically, we present a fully dynamic model which allows node mobility and churns (due to node arrivals/departure) and external environmental change. In contrast to the previous works on dynamic networks, our model defines the full dynamicity in terms of localized topological changes of each node and can tolerate some external environmental change. The external environment changes are captured by the random jamming method. We show that FD-Broadcast can achieve broadcasting in$O(D_{S})$rounds with a high probability guarantee under the assumption of constant dynamic rate in the SINR model, where$D_{S}$is the dynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. Moreover, the lower bound of dynamic broadcasting is proved to be$\Omega(D_{S})$, thus, FD-Broadcast is asymptotically optimal with high probability.

The core number of vertices is a basic index depicting cohesiveness of a graph, and has been widely used in large-scale graph analytics. In this paper, we study the update of core numbers of vertices in dynamic graphs with edge insertions/deletions, which is known as the core maintenance problem. Different from previous approaches that just focus on the case of single-edge insertion/deletion and sequentially handle the edges when multiple edges are inserted/deleted, we investigate the parallelism in the core maintenance procedure. Specifically, we show that if the inserted/deleted edges constitute a matching, the core number update with respect to each inserted/deleted edge can be handled in parallel. Based on this key observation, we propose parallel algorithms for core maintenance in both cases of edge insertions and deletions. Extensive experiments are conducted to evaluate the efficiency, stability, parallelism and scalability of our algorithms on different types of real-world, synthetic graphs and temporal networks. Comparing with former approaches, our algorithms can improve the core maintenance efficiency significantly.

Betweenness centrality of a node represents its influence over the spread of information in the network. It is normally defined as the ratio of the number of shortest paths passing through the node among all shortest paths. However, the spread of information may not just pass through the shortest paths which is captured by a new measure of betweenness centrality based on random walks [1]. The random walk betweenness centrality of a node means how often it is traversed by a random walk between all pairs of other nodes. In this paper, we propose an O(n log n) time distributed randomized approximation algorithm for calculating each node's random walk betweenness centrality with an approximation ratio (1-ϵ) where n is the number of nodes and ϵ is an arbitrarily small constant between 0 and 1. Our distributed algorithm is designed under the widely used CONGEST model, where each edge can only transfer O(log n) bits in each round. To our best knowledge, this is the first distributed algorithm for computing the random walk betweenness centrality. Moreover, we give a non-trivial lower bound for distributively computing the exact random walk betweenness centrality under the CONGEST model, which is Ω(n\log n +D) where D is the network diameter. This means exactly computing random walk betweenness cannot be done in sublinear time.

This paper initiates the studies of parallel algorithm for core maintenance in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which is widely used in large-scale graph analytics. We investigate the parallelism in the core update process when multiple edges and vertices are inserted. Specifically, we discover a structure called superior edge set, the insertion of edges in which can be processed in parallel. Based on the structure of superior edge set, an efficient parallel algorithm is then devised. To the best of our knowledge, the proposed algorithm is the first parallel one for the fundamental core maintenance problem. Finally, extensive experiments are conducted on different types of real-world and synthetic datasets, and the results illustrate the efficiency, stability and scalability of the proposed algorithm. The algorithm shows a significant speedup in the processing time compared with previous results that sequentially handle edge and vertex insertions.

Dynamic behavior is an essential part of wireless networking, due mobility, environmental changes or failures. We analyze a natural exponential backoff procedure to manage contention in a fading channel, in the presence of both node churn and link changes. We show that it attains a fast convergence, stabilizing contention from any state in logarithmic time. We use it to obtain optimal algorithm for Local Broadcast that even improves known results for the static case. The results illustrate the utility of carrier sensing, a stock feature of wireless nodes.

This paper presents the first distributed algorithm to construct a spanner for arbitrary ad hoc networks under the physical signal-to-interference-and-noise-ratio (SINR) interference model. Spanner construction is one of the most important techniques for topology control in wireless networks, which intends to find a sparse topology in which only a small number of links need to be maintained, without substantially degrading the path connecting any pair of the nodes in the network. Due to the non-local property of interference, constructing a spanner is challenging under the SINR model, especially when a local distributed algorithm is desired. We meet this challenge by proposing an efficient randomized distributed algorithm that can construct a spanner in O(log n log Γ) timeslots with a high probability, where n is the total number of nodes and Γ describes the ratio of the maximum distance to the minimum distance between nodes. The constructed spanner concurrently satisfies two most desirable properties: constant stretch and linear sparseness. Our algorithm employs a novel maximal independent set (MIS) procedure as a subroutine, which is crucial in achieving the time efficiency of spanner construction. The MIS algorithm improves the best known result of O(log2n) [33] to O(log n) and is of independent interest as the algorithm is applicable also to many other applications. We conduct simulations to verify the proposed spanner construction algorithm, and the results show that our algorithm also performs well in realistic environments.

Spanner construction is one of the most important techniques for topology control in wireless networks. A spanner can help not only to decrease the number of links and to maintain connectivity but also to ensure that the distance between any pair of communication nodes is within some constant factor from the shortest possible distance. Due to the non-locality, constructing a spanner is especially challenging under the physical interference model signal-to-interference-and-noise-ratio (SINR). In this paper, we develop two localized randomized algorithms SINR-directed-YG and SINR-undirected-YG to construct a directed Yao graph (YG) and an undirected YG in O(log n) (n is the number of wireless nodes) time slots with a high probability, in which each node is capable of performing successful local broadcasts to gather neighborhood information within a certain region and the SINR constraint is satisfied at all the steps of the algorithms. The resultant graph of SINR-undirected-YG, which is based on SINR-directed-YG, possesses a constant stretch factor 1/1-2 sin(π/c), where c > 6 is a constant. To the best of our knowledge, SINR-undirected-YG is the first spanner construction algorithm under SINR. We also obtain Yao-Yao graph under SINR. Extensive theoretical performance analysis and simulation study are carried out to verify the effectiveness and the efficiency of our proposed algorithms.

We study distributed algorithms for achieving efficient communications in the Rayleigh-fading Model. This model extends the popular deterministic SINR model using stochastic propagation to address fading effects observed in reality. Stochastic propagation greatly increases the difficulty of dealing with interference and collisions, especially in a local context without much global knowledge. We present a new technique called Inductive Coloring that can be used to schedule fast transmissions with Rayleigh-fading interference. The computation of inductive coloring takes only O(log2n) time with the proposed distributed algorithm, where n is the number of nodes in the network. We illustrate the power of inductive coloring by giving algorithms for implementing two basic communication primitives. The first primitive is Local Broadcast (LB), which can work in a MAC layer and has been widely studied in different interference models. The proposed algorithm for LB matches the fastest one under the simpler SINR model. The second primitive is Single-Reception (SR), which is to make each node receive at least one message from its neighbors. The proposed algorithm can implement SR in O(log2n) rounds. To illustrate the versatility of the SR primitive, we use the primitive to derive efficient algorithms for information broadcast and function computations. We conduct simulations to verify all the proposed algorithms, and the results show that the algorithms also perform well in realistic environments.

announcement Share on Brief Announcement: Data Dissemination in Unified Dynamic Wireless Networks Authors: Magnus M. Halldórsson Reykjavik University, Reykjavik, Iceland Reykjavik University, Reykjavik, IcelandView Profile , Tigran Tonoyan Reykjavik University, Reykjavik, Iceland Reykjavik University, Reykjavik, IcelandView Profile , Yuexuan Wang Zhejiang University, Hangzhou, China Zhejiang University, Hangzhou, ChinaView Profile , Dongxiao Yu Huazhong University of Science and Technology, Wuhan, China Huazhong University of Science and Technology, Wuhan, ChinaView Profile Authors Info & Claims PODC '16: Proceedings of the 2016 ACM Symposium on Principles of Distributed ComputingJuly 2016 Pages 199–201https://doi.org/10.1145/2933057.2933065Published:25 July 2016Publication History 1citation132DownloadsMetricsTotal Citations1Total Downloads132Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

As a distributed system, a wireless network, usually faces a complex environment (transient faults and topology changes occur frequently). The connected dominating set (CDS) problem has been widely studied due to its important applications in wireless communication and networks, especially the important role as a virtual backbone for efficient routing. In this paper, under SINR (Signal-to-Interference-plus-Noise-Ratio) model, we propose a distributed self-stabilizing maximal independent set (MIS) algorithm (DSSMIS). Based on DSSMIS, we design a distributed self-stabilizing algorithm (DSSCDS) for CDS construction with constant approximation within O(log n) rounds. To best of our knowledge, this is the first self-stabilizing CDS algorithm under SINR model.

Discrete beeping is an extremely rigorous local broadcast model depending only on carrier sensing. It describes an anonymous broadcast network where the nodes do not need unique identifiers and have no knowledge about the topology and size of the network. Within such a model, time is divided into slots, and nodes can either beep or keep silent at each slot. We consider the problem of constructing a minimum dominating set (MDS) and a minimum connected dominating set (MCDS), respectively, under the discrete beeping model in this paper. By assuming that an upper bound N of the network size is known, we first propose and analyze a distributed synchronous algorithm termed BMDS for constructing a minimum dominating set (MDS) and then propose a distributed synchronous algorithm BCDS for CDS construction based on a maximal independent set (MIS) algorithm and a weakly CDS (WCDS). To our best knowledge, we are the first to study the MCDS construction under the discrete beeping model. We prove that the time complexity of BMDS is O(log2N) rounds with constant approximation ratio of at most 2, and BCDS can converge to a CDS within O(log3N) rounds.

This paper initiates the study of distributed information exchange in multi-channel wireless ad hoc networks. Information exchange is a basic operation in which each node of the network sends an information packet to other nodes within a specific distance R. Our study is motivated by the increasing presence and popularity of wireless networks and devices that operate on multiple channels. Consequently, there is a need for a better understanding of how and by how much multiple channels can improve communication. Based on the SINR interference model, we propose a multi-channel network model which incorporates certain features commonly seen in wireless ad hoc networks, including asynchrony, little non-local knowledge, limited message size, and limited power control. We then present a randomized algorithm that can accomplish information exchange in O ((Δ/F + Δlog n/P) log n + log Δ log n) timeslots with high probability, where n is the number of nodes in the network, Δ is the maximum number of nodes within the range R, F is the number of available channels and P is the maximum number of packets that can fit in a message. Our algorithm significantly surpasses the best known results in single-channel networks, achieving a Θ(F) times speedup if Δ and P are sufficiently large. We conducted empirical studies that confirmed the performance of the proposed algorithm as derived in the analysis.

In communication networks such as social networks, wireless networks and biology networks, it is of importance to find all cliques which can help understand the network topology. The clique structures can also be utilized in facilitating message forwarding in wireless networks. For instance, using a set of cliques of maximal and disjoint, one of the nodes in each clique can be elected to forward messages, by which the duplicate message transmissions can be efficiently reduced. Further, it is well known that the maximum clique problem (MCP) is closely related to the maximum independent set and the vertex cover problems. In recent years, the fundamental problem of finding maximal cliques or maximum cliques has attracted lots of attentions. However, few of these works focus on distributed solutions in wireless networks. In this paper, we pay our attention to this missing corner of research. Specifically, we first give a distributed algorithm which can compute all maximal cliques in a wireless network represented by a graph. The algorithm takes O(n) time and uses O(mn) messages, where n is the number of nodes and m the number of edges. Then, with the proposed algorithms MCP (maximum clique problem) and UMCP (unique MCP), we show that a unique maximum clique can be selected from all maximal cliques in O(n) rounds and using O(mn) messages. To the best of our knowledge, our algorithms are the first deterministic distributed solutions for MCP in wireless networks.

We explore the utility of multiple channels of communication in wireless networks under the SINR model of interference. The central question is whether multiple channels can result in linear speedup, up to some fundamental limit. We answer this question affirmatively for the data aggregation problem, perhaps the most fundamental problem in sensor networks. To achieve this, we form a hierarchical structure of independent interest, and illustrate its versatility by obtaining a new algorithm with linear speedup for the node coloring problem.

Multiple-message broadcast is a generalization of the traditional broadcast problem. It is to disseminate k distinct (1 ≤ k ≤ n) messages stored at k arbitrary nodes to the entire network with the fewest timeslots. In this paper, we study this basic communication primitive in unstructured wireless networks under the physical interference model (also known as the SINR model). The unstructured wireless network assumes unknown network topology, no collision detection and asynchronous communications. Our proposed randomized distributed algorithm can accomplish multiple-message broadcast in O((D + k) log n + log2n) timeslots with high probability, where D is the network diameter and n is the number of nodes in the network. To our best knowledge, this work is the first one to consider distributively implementing multiple-message broadcasting in unstructured wireless networks under a global interference model, which may shed some light on how to efficiently solve in general a “global” problem in a “local” fashion with “global” interference constraints in asynchronous wireless ad hoc networks. Apart from the algorithm, we also show an Ω(D+k+log n) lower bound for randomized distributed multiple message broadcast algorithms under the assumed network model.

In this paper, we study incentive mechanisms for retrieving information from networked agents. Following the model in [Kleinberg and Raghavan 2005], the agents are represented as nodes in an infinite tree, which is generated by a random branching process. A query is issued by the root, and each node possesses an answer with an independent probability p=1/n. Further, each node in the tree acts strategically to maximize its own payoff. In order to encourage the agents to participate in the information acquisition process, an incentive mechanism is needed to reward agents who provide the information as well as agents who help to facilitate such acquisition.

Minimizing latency and energy consumption is the prime objective of the design of data aggregation in battery-powered wireless networks. A tradeoff exists between the aggregation latency and the energy consumption, which has been widely studied under the protocol interference model. There has been, however, no investigation of the tradeoff under the physical interference model that is known to capture more accurately the characteristics of wireless interferences. When coupled with the technique of successive interference cancellation, by which a receiver may recover signals from multiple simultaneous senders, the model can lead to much reduced latency but increased energy usage. In this paper, we investigate the latency-energy tradeoff for data aggregation in wireless sensor networks under the physical interference model and using successive interference cancellation. We present theoretical lower bounds on both latency and energy as well as their tradeoff, and give an efficient approximation algorithm that can achieve the asymptotical optimum in both aggregation latency and latency-energy tradeoff. We show that our algorithm can significantly reduce the aggregation latency, for which the energy consumption is kept at its lowest possible level.

The unstructured multi-hop radio network model, with asynchronous wake-up, no collision detection and little knowledge on the network topology, is proposed for capturing the particularly harsh characteristics of initially deployed wireless adhoc and sensor networks. In this paper, assuming such a practical model, we study a fundamental problem of both theoretical and practical interests--the local broadcasting problem. Given a set of nodes V where each node wants to broadcast a message to all its neighbors that are within a certain local broadcasting range R, the problem is to schedule all these requests in the fewest timeslots. By adopting the physical interference mode land without any knowledge on neighborhood, we give a new randomized distributed approximation algorithm for the local broadcasting problem with approximation ratio O (log n) where nis the number of nodes. This distributed approximation algorithm improves the state-of-the-art result in [22] by a logarithmic factor.